Fundus image quality assessment: survey, challenges, and future scope
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paper_title: The Analysis of Image Contrast: From Quality Assessment to Automatic Enhancement

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Proper contrast change can improve the perceptual quality of most images, but it has largely been overlooked in the current research of image quality assessment (IQA). To fill this void, we in this paper first report a new large dedicated contrast-changed image database (CCID2014), which includes 655 images and associated subjective ratings recorded from 22 inexperienced observers. We then present a novel reduced-reference image quality metric for contrast change (RIQMC) using phase congruency and statistics information of the image histogram. Validation of the proposed model is conducted on contrast related CCID2014, TID2008, CSIQ and TID2013 databases, and results justify the superiority and efficiency of RIQMC over a majority of classical and state-of-the-art IQA methods. Furthermore, we combine aforesaid subjective and objective assessments to derive the RIQMC based Optimal HIstogram Mapping (ROHIM) for automatic contrast enhancement, which is shown to outperform recently developed enhancement technologies.
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paper_title: Deep CNN-Based Blind Image Quality Predictor

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Image recognition based on convolutional neural networks (CNNs) has recently been shown to deliver the state-of-the-art performance in various areas of computer vision and image processing. Nevertheless, applying a deep CNN to no-reference image quality assessment (NR-IQA) remains a challenging task due to critical obstacles, i.e., the lack of a training database. In this paper, we propose a CNN-based NR-IQA framework that can effectively solve this problem. The proposed method—deep image quality assessor (DIQA)—separates the training of NR-IQA into two stages: 1) an objective distortion part and 2) a human visual system-related part. In the first stage, the CNN learns to predict the objective error map, and then the model learns to predict subjective score in the second stage. To complement the inaccuracy of the objective error map prediction on the homogeneous region, we also propose a reliability map. Two simple handcrafted features were additionally employed to further enhance the accuracy. In addition, we propose a way to visualize perceptual error maps to analyze what was learned by the deep CNN model. In the experiments, the DIQA yielded the state-of-the-art accuracy on the various databases.
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paper_title: Deep Neural Networks for No-Reference and Full-Reference Image Quality Assessment

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We present a deep neural network-based approach to image quality assessment (IQA). The network is trained end-to-end and comprises ten convolutional layers and five pooling layers for feature extraction, and two fully connected layers for regression, which makes it significantly deeper than related IQA models. Unique features of the proposed architecture are that: 1) with slight adaptations it can be used in a no-reference (NR) as well as in a full-reference (FR) IQA setting and 2) it allows for joint learning of local quality and local weights, i.e., relative importance of local quality to the global quality estimate, in an unified framework. Our approach is purely data-driven and does not rely on hand-crafted features or other types of prior domain knowledge about the human visual system or image statistics. We evaluate the proposed approach on the LIVE, CISQ, and TID2013 databases as well as the LIVE In the wild image quality challenge database and show superior performance to state-of-the-art NR and FR IQA methods. Finally, cross-database evaluation shows a high ability to generalize between different databases, indicating a high robustness of the learned features.
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paper_title: Blind Stereoscopic 3D Image Quality Assessment via Analysis of Naturalness, Structure, and Binocular Asymmetry

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Abstract Over recent years, stereoscopic three dimensional (S3D) images have grown explosively and received increasing attention. Quality assessment, as the fundamental problem, plays an important role in promoting the prevalence of S3D images as well as the associated products. In this paper, an effective blind quality assessment method of S3D images is proposed via analysis of naturalness, structure, and binocular asymmetry. To be specific, given that natural images obey certain regular statistical properties, natural scene statistic (NSS) features of left and right views are first extracted to quantify the naturalness. Second, by considering binocular visual characteristics, statistical features are extracted from a created cyclopean map. Moreover, gray level co-occurrence matrix (GLCM) is utilized to capture quality-sensitive features from the cyclopean phase map. Third, to quantify the asymmetric distortion, a simple but effective measurement is utilized, i.e., calculating the similarity between left and right views as well as statistical features of their difference map. Finally, all extracted quality-sensitive features are combined, and trained together with the subjective ratings to form a regression model using support vector regression (SVR). Experimental results on four publicly available databases (two symmetrically distorted databases and two asymmetrically distorted databases) demonstrate that the proposed method is superior to several mainstream image quality assessment (IQA) metrics.
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paper_title: Deep Convolutional Neural Models for Picture-Quality Prediction: Challenges and Solutions to Data-Driven Image Quality Assessment

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Convolutional neural networks (CNNs) have been shown to deliver standout performance on a wide variety of visual information processing applications. However, this rapidly developing technology has only recently been applied with systematic energy to the problem of picture-quality prediction, primarily because of limitations imposed by a lack of adequate ground-truth human subjective data. This situation has begun to change with the development of promising data-gathering methods that are driving new approaches to deep-learning-based perceptual picture-quality prediction. Here, we assay progress in this rapidly evolving field, focusing, in particular, on new ways to collect large quantities of ground-truth data and on recent CNN-based picture-quality prediction models that deliver excellent results in a large, real-world, picture-quality database.
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paper_title: Fully Deep Blind Image Quality Predictor

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In general, owing to the benefits obtained from original information, full-reference image quality assessment (FR-IQA) achieves relatively higher prediction accuracy than no-reference image quality assessment (NR-IQA). By fully utilizing reference images, conventional FR-IQA methods have been investigated to produce objective scores that are close to subjective scores. In contrast, NR-IQA does not consider reference images; thus, its performance is inferior to that of FR-IQA. To alleviate this accuracy discrepancy between FR-IQA and NR-IQA methods, we propose a blind image evaluator based on a convolutional neural network (BIECON). To imitate FR-IQA behavior, we adopt the strong representation power of a deep convolutional neural network to generate a local quality map, similar to FR-IQA. To obtain the best results from the deep neural network, replacing hand-crafted features with automatically learned features is necessary. To apply the deep model to the NR-IQA framework, three critical problems must be resolved: 1) lack of training data; 2) absence of local ground truth targets; and 3) different purposes of feature learning. BIECON follows the FR-IQA behavior using the local quality maps as intermediate targets for conventional neural networks, which leads to NR-IQA prediction accuracy that is comparable with that of state-of-the-art FR-IQA methods.
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paper_title: Saliency-induced reduced-reference quality index for natural scene and screen content images

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Abstract Massive content composed of both natural scene and screen content has been generated with the increasing use of wireless computing and cloud computing, which call for general image quality assessment (IQA) measures working for both natural scene images (NSIs) and screen content images (SCIs). In this paper, we develop a saliency-induced reduced-reference (SIRR) IQA measure for both NSIs and SCIs. Image quality and visual saliency are two widely studied and closely related research topics. Traditionally, visual saliency is often used as a weighting map in the final pooling stage of IQA. Instead, we detect visual saliency as a quality feature since different types and levels of degradation can strongly influence saliency detection. Image quality is described by the similarity between two images’ saliency maps. In SIRR, saliency is detected through a binary image descriptor called “image signature”, which significantly reduces the reference data. We perform extensive experiments on five large-scale NSI quality assessment databases including LIVE, TID2008, CSIQ, LIVEMD, CID2013, as well as two recently constructed SCI QA databases, i.e., SIQAD and QACS. Experimental results show that SIRR is comparable to state-of-the-art full-reference and reduced-reference IQA measures in NSIs, and it can outperform most competitors in SCIs. The most important is that SIRR is a cross-content-type measure, which works efficiently for both NSIs and SCIs. The MATLAB source code of SIRR will be publicly available with this paper.
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paper_title: Subjective video quality assessment methods for recognition tasks

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To develop accurate objective measurements (models) for video quality assessment, subjective data is traditionally ::: collected via human subject testing. The ITU has a series of Recommendations that address methodology for performing ::: subjective tests in a rigorous manner. These methods are targeted at the entertainment application of video. However, ::: video is often used for many applications outside of the entertainment sector, and generally this class of video is used to ::: perform a specific task. Examples of these applications include security, public safety, remote command and control, ::: and sign language. For these applications, video is used to recognize objects, people or events. The existing methods, ::: developed to assess a person's perceptual opinion of quality, are not appropriate for task-based video. The Institute for ::: Telecommunication Sciences, under a program from the Department of Homeland Security and the National Institute for ::: Standards and Technology's Office of Law Enforcement, has developed a subjective test method to determine a person's ::: ability to perform recognition tasks using video, thereby rating the quality according to the usefulness of the video ::: quality within its application. This new method is presented, along with a discussion of two examples of subjective tests ::: using this method.
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paper_title: Gradient Magnitude Similarity Deviation: A Highly Efficient Perceptual Image Quality Index

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It is an important task to faithfully evaluate the perceptual quality of output images in many applications, such as image compression, image restoration, and multimedia streaming. A good image quality assessment (IQA) model should not only deliver high quality prediction accuracy, but also be computationally efficient. The efficiency of IQA metrics is becoming particularly important due to the increasing proliferation of high-volume visual data in high-speed networks. We present a new effective and efficient IQA model, called gradient magnitude similarity deviation (GMSD). The image gradients are sensitive to image distortions, while different local structures in a distorted image suffer different degrees of degradations. This motivates us to explore the use of global variation of gradient based local quality map for overall image quality prediction. We find that the pixel-wise gradient magnitude similarity (GMS) between the reference and distorted images combined with a novel pooling strategy-the standard deviation of the GMS map-can predict accurately perceptual image quality. The resulting GMSD algorithm is much faster than most state-of-the-art IQA methods, and delivers highly competitive prediction accuracy. MATLAB source code of GMSD can be downloaded at http://www4.comp.polyu.edu.hk/~cslzhang/IQA/GMSD/GMSD.htm.
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paper_title: Evaluating quality of compressed medical images : SNR, subjective rating, and diagnostic accuracy : Data compression

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Compressing a digital image can facilitate its transmission, storage, and processing. As radiology departments become increasingly digital, the quantities fo their imaging data are forcing consideration of compression in picture archiving and communication systems. Significant compression is achievable anly by lossy algorithms, which do not permit the exact recovery of the original images
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paper_title: Blind Image Quality Assessment: A Natural Scene Statistics Approach in the DCT Domain

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We develop an efficient general-purpose blind/no-reference image quality assessment (IQA) algorithm using a natural scene statistics (NSS) model of discrete cosine transform (DCT) coefficients. The algorithm is computationally appealing, given the availability of platforms optimized for DCT computation. The approach relies on a simple Bayesian inference model to predict image quality scores given certain extracted features. The features are based on an NSS model of the image DCT coefficients. The estimated parameters of the model are utilized to form features that are indicative of perceptual quality. These features are used in a simple Bayesian inference approach to predict quality scores. The resulting algorithm, which we name BLIINDS-II, requires minimal training and adopts a simple probabilistic model for score prediction. Given the extracted features from a test image, the quality score that maximizes the probability of the empirically determined inference model is chosen as the predicted quality score of that image. When tested on the LIVE IQA database, BLIINDS-II is shown to correlate highly with human judgments of quality, at a level that is competitive with the popular SSIM index.
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paper_title: A new reduced-reference image quality assessment using structural degradation model

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Image quality assessment (IQA) is an important research area in image processing. Reduced-reference (RR) IQA methods contained therein mainly aim to estimate image quality degradations with partial information about the reference image. Following the remarkable achievement of SSIM, structural information has been recognized as one key factor, and has aroused many image quality metrics so far. In this paper, we design a structural degradation model (SDM). Then, the quality score of an image is defined as a nonlinear combination, or SVM based integration, of distance between the structural degradation information of the original and distorted images. Accordingly, a new RR IQA approach using the SDM model is exploited. Experimental results on LIVE database are provided to justify the superior prediction accuracy performance of the proposed method as compared to three significant image quality metrics, PSNR, SSIM and FEDM.
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paper_title: Blind Image Quality Assessment via Deep Learning

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This paper investigates how to blindly evaluate the visual quality of an image by learning rules from linguistic descriptions. Extensive psychological evidence shows that humans prefer to conduct evaluations qualitatively rather than numerically. The qualitative evaluations are then converted into the numerical scores to fairly benchmark objective image quality assessment (IQA) metrics. Recently, lots of learning-based IQA models are proposed by analyzing the mapping from the images to numerical ratings. However, the learnt mapping can hardly be accurate enough because some information has been lost in such an irreversible conversion from the linguistic descriptions to numerical scores. In this paper, we propose a blind IQA model, which learns qualitative evaluations directly and outputs numerical scores for general utilization and fair comparison. Images are represented by natural scene statistics features. A discriminative deep model is trained to classify the features into five grades, corresponding to five explicit mental concepts, i.e., excellent, good, fair, poor, and bad. A newly designed quality pooling is then applied to convert the qualitative labels into scores. The classification framework is not only much more natural than the regression-based models, but also robust to the small sample size problem. Thorough experiments are conducted on popular databases to verify the model’s effectiveness, efficiency, and robustness.
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paper_title: Subjective and Objective Quality Assessment of Image: A Survey

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With the increasing demand for image-based applications, the efficient and reliable evaluation of image quality has increased in importance. Measuring the image quality is of fundamental importance for numerous image processing applications, where the goal of image quality assessment (IQA) methods is to automatically evaluate the quality of images in agreement with human quality judgments. Numerous IQA methods have been proposed over the past years to fulfill this goal. In this paper, a survey of the quality assessment methods for conventional image signals, as well as the newly emerged ones, which includes the high dynamic range (HDR) and 3-D images, is presented. A comprehensive explanation of the subjective and objective IQA and their classification is provided. Six widely used subjective quality datasets, and performance measures are reviewed. Emphasis is given to the full-reference image quality assessment (FR-IQA) methods, and 9 often-used quality measures (including mean squared error (MSE), structural similarity index (SSIM), multi-scale structural similarity index (MS-SSIM), visual information fidelity (VIF), most apparent distortion (MAD), feature similarity measure (FSIM), feature similarity measure for color images (FSIMC), dynamic range independent measure (DRIM), and tone-mapped images quality index (TMQI)) are carefully described, and their performance and computation time on four subjective quality datasets are evaluated. Furthermore, a brief introduction to 3-D IQA is provided and the issues related to this area of research are reviewed.
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paper_title: Pseudo no reference image quality metric using perceptual data hiding

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Regarding the important constraints due to subjective quality assessment, objective image quality assessment has recently been extensively studied. Such metrics are usually of three kinds, they might be Full Reference (FR), Reduced Reference (RR) or No Reference (NR) metrics. We focus here on a new technique, which recently appeared in quality assessment context: data-hiding-based image quality metric. Regarding the amount of data to be transmitted for quality assessment purpose, watermarking based techniques are considered as pseudo noreference metric: A little overhead due to the embedded watermark is added to the image. Unlike most existing techniques, the proposed embedding method exploits an advanced perceptual model in order to optimize both the data embedding and extraction. A perceptually weighted watermark is embedded into the host image, and an evaluation of this watermark allows to assess the host image's quality. In such context, the watermark robustness is crucial; it must be suffciently robust to be detected after very strong distortions, but it must also be suffciently fragile to be degraded along with the host image. In other words, the watermark distortion must be proportional to the image's distortion. Our work is compared to existing standard RR and NR metrics in terms of both the correlation with subjective assessment and of data overhead induced by the mark.
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paper_title: No-Reference Image Quality Assessment in the Spatial Domain

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We propose a natural scene statistic-based distortion-generic blind/no-reference (NR) image quality assessment (IQA) model that operates in the spatial domain. The new model, dubbed blind/referenceless image spatial quality evaluator (BRISQUE) does not compute distortion-specific features, such as ringing, blur, or blocking, but instead uses scene statistics of locally normalized luminance coefficients to quantify possible losses of "naturalness" in the image due to the presence of distortions, thereby leading to a holistic measure of quality. The underlying features used derive from the empirical distribution of locally normalized luminances and products of locally normalized luminances under a spatial natural scene statistic model. No transformation to another coordinate frame (DCT, wavelet, etc.) is required, distinguishing it from prior NR IQA approaches. Despite its simplicity, we are able to show that BRISQUE is statistically better than the full-reference peak signal-to-noise ratio and the structural similarity index, and is highly competitive with respect to all present-day distortion-generic NR IQA algorithms. BRISQUE has very low computational complexity, making it well suited for real time applications. BRISQUE features may be used for distortion-identification as well. To illustrate a new practical application of BRISQUE, we describe how a nonblind image denoising algorithm can be augmented with BRISQUE in order to perform blind image denoising. Results show that BRISQUE augmentation leads to performance improvements over state-of-the-art methods. A software release of BRISQUE is available online: http://live.ece.utexas.edu/research/quality/BRISQUE_release.zip for public use and evaluation.
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paper_title: Convolutional Neural Networks for No-Reference Image Quality Assessment

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In this work we describe a Convolutional Neural Network (CNN) to accurately predict image quality without a reference image. Taking image patches as input, the CNN works in the spatial domain without using hand-crafted features that are employed by most previous methods. The network consists of one convolutional layer with max and min pooling, two fully connected layers and an output node. Within the network structure, feature learning and regression are integrated into one optimization process, which leads to a more effective model for estimating image quality. This approach achieves state of the art performance on the LIVE dataset and shows excellent generalization ability in cross dataset experiments. Further experiments on images with local distortions demonstrate the local quality estimation ability of our CNN, which is rarely reported in previous literature.
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paper_title: Analysis of Structural Characteristics for Quality Assessment of Multiply Distorted Images

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Perceptual image quality assessment (IQA) plays an important role in numerous applications, including image restoration, compression, enhancement, and others. Although many works have been conducted on individually distorted IQA problems and have achieved encouraging results, few studies have been conducted on multiple distorted (MD) IQA problems. Thus, limited progress has been made. In this paper, we propose a novel no reference image quality assessment (NR-IQA) method, named improved multiscale local binary pattern (IMLBP), for addressing multiply distorted IQA problems. The image structures are sensitive to image distortions, which motivates us to utilize the structural characteristics for overall image quality prediction. We improved the local binary pattern (LBP) by considering the human visual mechanism to better extract the structural information. The IMLBP contains two parts, the LBP and the radius difference LBP (DLBP). The DLBP reflects the values’ changes in the radial direction. Specifically, when the radius value is small, the proposed descriptor is computed to represent microstructural information. Conversely, it represents macrostructural information when the radius becomes large. Moreover, to better mimick the human visual mechanism, the IMLBP is computed with the multiscale strategy and the operation is based on a patch unit whose size is proportional to the radius value. The frequency histogram of feature maps is transformed to feature vectors. Subsequently, a predictable function trained by the support vector regression is used to infer the overall quality score. Experimental results show that the proposed method outperforms most state-of-the-art IQA metrics on publicly available multiply distorted image databases.
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paper_title: Biologically Inspired Blind Quality Assessment of Tone-Mapped Images

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Currently, many tone mapping operators (TMOs) have been provided to compress high dynamic range images to low dynamic range (LDR) images for visualizing them on the common displays. Since quality degradation is inevitably induced by compression, how to evaluate the obtained LDR images is indeed a headache problem. Until now, only a few full reference (FR) image quality assessment metrics have been proposed. However, they highly depend on reference image and neglect human visual system characteristics, hindering the practical applications. In this paper, we propose an effective blind quality assessment method of tone-mapped image without access to reference image. Inspired by that the performance of existing TMOs largely depend on the brightness and chromatic and structural properties of a scene, we evaluate the perceptual quality from the perspective of color information processing in the brain. Specifically, motivated by the physiological and psychological evidence, we simulate the responses of single-opponent (SO) and double-opponent (DO) cells, which play an important role in the processing of the color information. To represent the textural information, we extract three features from gray-level co-occurrence matrix (GLCM) calculated from SO responses. Meanwhile, both GLCM and local binary pattern descriptor are employed to extract texture and structure in the responses of DO cells. All these extracted features and associated subjective ratings are learned to reveal the connection between feature space and human opinion score. Extensive experiments demonstrate that the proposed method outperforms the state-of-the-art blind quality assessment methods and is comparable with the popular FR methods on two recently published tone-mapped image databases.
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paper_title: Fourier Transform-Based Scalable Image Quality Measure

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We present a new image quality assessment algorithm based on the phase and magnitude of the 2-D discrete Fourier transform. The basic idea is to compare the phase and magnitude of the reference and distorted images to compute the quality score. However, it is well known that the human visual system's sensitivity to different frequency components is not the same. We accommodate this fact via a simple yet effective strategy of non-uniform binning of the frequency components. This process also leads to reduced space representation of the image thereby enabling the reduced-reference (RR) prospects of the proposed scheme. We employ linear regression to integrate the effects of the changes in phase and magnitude. In this way, the required weights are determined via proper training and hence more convincing and effective. Last, using the fact that phase usually conveys more information than magnitude, we use only the phase for RR quality assessment. This provides the crucial advantage of further reduction in the required amount of reference image information. The proposed method is, therefore, further scalable for RR scenarios. We report extensive experimental results using a total of nine publicly available databases: seven image (with a total of 3832 distorted images with diverse distortions) and two video databases (totally 228 distorted videos). These show that the proposed method is overall better than several of the existing full-reference algorithms and two RR algorithms. Additionally, there is a graceful degradation in prediction performance as the amount of reference image information is reduced thereby confirming its scalability prospects. To enable comparisons and future study, a Matlab implementation of the proposed algorithm is available at http://www.ntu.edu.sg/home/wslin/reduced_phase.rar.
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paper_title: A deep neural network for image quality assessment

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This paper presents a no reference image (NR) quality assessment (IQA) method based on a deep convolutional neural network (CNN). The CNN takes unpreprocessed image patches as an input and estimates the quality without employing any domain knowledge. By that, features and natural scene statistics are learnt purely data driven and combined with pooling and regression in one framework. We evaluate the network on the LIVE database and achieve a linear Pearson correlation superior to state-of-the-art NR IQA methods. We also apply the network to the image forensics task of decoder-sided quantization parameter estimation and also here achieve correlations of r = 0.989.
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paper_title: Image quality assessment: from error visibility to structural similarity

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Objective methods for assessing perceptual image quality traditionally attempted to quantify the visibility of errors (differences) between a distorted image and a reference image using a variety of known properties of the human visual system. Under the assumption that human visual perception is highly adapted for extracting structural information from a scene, we introduce an alternative complementary framework for quality assessment based on the degradation of structural information. As a specific example of this concept, we develop a structural similarity index and demonstrate its promise through a set of intuitive examples, as well as comparison to both subjective ratings and state-of-the-art objective methods on a database of images compressed with JPEG and JPEG2000. A MATLAB implementation of the proposed algorithm is available online at http://www.cns.nyu.edu//spl sim/lcv/ssim/.
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paper_title: A deep learning approach to document image quality assessment

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This paper proposes a deep learning approach for document image quality assessment. Given a noise corrupted document image, we estimate its quality score as a prediction of OCR accuracy. First the document image is divided into patches and non-informative patches are sifted out using Otsu's binarization technique. Second, quality scores are obtained for all selected patches using a Convolutional Neural Network (CNN), and the patch scores are averaged over the image to obtain the document score. The proposed CNN contains two layers of convolution, location blind max-min pooling, and Rectified Linear Units in the fully connected layers. Experiments on two document quality datasets show our method achieved the state of the art performance.
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paper_title: Visual Importance and Distortion Guided Deep Image Quality Assessment Framework

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In this paper, we tackle the problem of no-reference image quality assessment (IQA). A learning-based IQA framework “VIDGIQA” is proposed, which extracts quality features from the input image and regresses the visual quality on these features. Since different distortions lead to different visual perceptions in the human visual system, distortion information is adopted to guide the feature learning process together with the human quality scores. Besides, a regression method is proposed to model and estimate the visual importance weights of all local regions, which can effectively improve the performance. More importantly, all these operations are integrated into one deep neural network, so that they can be jointly optimized and well cooperate with each other. Experiments were conducted to demonstrate the power of the proposed method on several datasets, including the LIVE dataset [1] , the TID 2013 dataset [2] , the LIVE multiply distorted IQA dataset [3] , CSIQ [4] , and the LIVE in the wild image quality database [5] . The proposed method achieves 0.969 and 0.973 on the LIVE dataset [1] in terms of the spearman rank-order correlation coefficient and the Pearson linear correlation coefficient, respectively, which outperforms the state-of-the-art methods.
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paper_title: Blind Image Quality Assessment: From Natural Scene Statistics to Perceptual Quality

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Our approach to blind image quality assessment (IQA) is based on the hypothesis that natural scenes possess certain statistical properties which are altered in the presence of distortion, rendering them un-natural; and that by characterizing this un-naturalness using scene statistics, one can identify the distortion afflicting the image and perform no-reference (NR) IQA. Based on this theory, we propose an (NR)/blind algorithm-the Distortion Identification-based Image Verity and INtegrity Evaluation (DIIVINE) index-that assesses the quality of a distorted image without need for a reference image. DIIVINE is based on a 2-stage framework involving distortion identification followed by distortion-specific quality assessment. DIIVINE is capable of assessing the quality of a distorted image across multiple distortion categories, as against most NR IQA algorithms that are distortion-specific in nature. DIIVINE is based on natural scene statistics which govern the behavior of natural images. In this paper, we detail the principles underlying DIIVINE, the statistical features extracted and their relevance to perception and thoroughly evaluate the algorithm on the popular LIVE IQA database. Further, we compare the performance of DIIVINE against leading full-reference (FR) IQA algorithms and demonstrate that DIIVINE is statistically superior to the often used measure of peak signal-to-noise ratio (PSNR) and statistically equivalent to the popular structural similarity index (SSIM). A software release of DIIVINE has been made available online: http://live.ece.utexas.edu/research/quality/DIIVINE_release.zip for public use and evaluation.
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paper_title: Seven Challenges in Image Quality Assessment: Past, Present, and Future Research

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Image quality assessment (IQA) has been a topic of intense research over the last several decades. With each year comes an increasing number of new IQA algorithms, extensions of existing IQA algorithms, and applications of IQA to other disciplines. In this article, I first provide an up-to-date review of research in IQA, and then I highlight several open challenges in this field. The first half of this article provides discuss key properties of visual perception, image quality databases, existing full-reference, no-reference, and reduced-reference IQA algorithms. Yet, despite the remarkable progress that has been made in IQA, many fundamental challenges remain largely unsolved. The second half of this article highlights some of these challenges. I specifically discuss challenges related to lack of complete perceptual models for: natural images, compound and suprathreshold distortions, and multiple distortions, and the interactive effects of these distortions on the images. I also discuss challenges related to IQA of images containing nontraditional, and I discuss challenges related to the computational efficiency. The goal of this article is not only to help practitioners and researchers ::: keep abreast of the recent advances in IQA, but to also raise awareness of the key limitations of current IQA knowledge.
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paper_title: A Statistical Evaluation of Recent Full Reference Image Quality Assessment Algorithms

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Measurement of visual quality is of fundamental importance for numerous image and video processing applications, where the goal of quality assessment (QA) algorithms is to automatically assess the quality of images or videos in agreement with human quality judgments. Over the years, many researchers have taken different approaches to the problem and have contributed significant research in this area and claim to have made progress in their respective domains. It is important to evaluate the performance of these algorithms in a comparative setting and analyze the strengths and weaknesses of these methods. In this paper, we present results of an extensive subjective quality assessment study in which a total of 779 distorted images were evaluated by about two dozen human subjects. The "ground truth" image quality data obtained from about 25 000 individual human quality judgments is used to evaluate the performance of several prominent full-reference image quality assessment algorithms. To the best of our knowledge, apart from video quality studies conducted by the Video Quality Experts Group, the study presented in this paper is the largest subjective image quality study in the literature in terms of number of images, distortion types, and number of human judgments per image. Moreover, we have made the data from the study freely available to the research community . This would allow other researchers to easily report comparative results in the future
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paper_title: Deep learning network for blind image quality assessment

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Nowadays, blind image quality assessment (BIQA) has been intensively studied with machine learning, such as support vector machine (SVM) and k-means. Existing BIQA metrics, however, do not perform robust for various kinds of distortion types. We believe this problem is because those frequently used traditional machine learning techniques exploit shallow architectures, which only contain one single layer of nonlinear feature transformation, and thus cannot highly mimic the mechanism of human visual perception to image quality. The recent advance of deep neural network (DNN) can help to solve this problem, since the DNN is found to better capture the essential attributes of images. We in this paper therefore introduce a new Deep learning based Image Quality Index (DIQI) for blind quality assessment. Extensive studies are conducted on the new TID2013 database and confirm the effectiveness of our DIQI relative to classical full-reference and state-of-the-art reduced- and no-reference IQA approaches.
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paper_title: A Study on Quality Assessment for Medical Ultrasound Video Compressed via HEVC

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The quality of experience and quality of service provided in the healthcare sector are critical in evaluating the reliable delivery of the healthcare services provided. Medical images and videos play a major role in modern e-health services and have become an integral part of medical data communication systems. The quality evaluation of medical images and videos is an essential process, and one of the ways of addressing it is via the use of quality metrics. In this paper, we evaluate the performance of seven state-of-the-art video quality metrics with respect to compressed medical ultrasound video sequences. We study the performance of each video quality metric in representing the diagnostic quality of the video, by evaluating the correlation of each metric with the subjective opinions of medical experts. The results indicate that the visual information fidelity, structural similarity index, and universal quality index metrics show good correlation with the subjective scores provided by medical experts. The tests also investigate the performance of the emerging video compression standard, high-efficiency video coding-HEVC, for medical ultrasound video compression. The results show that, using HEVC with the considered ultrasound video sequences, a diagnostically reliable compressed ultrasound video can be obtained for compression with values of the quantization parameter up to 35.
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paper_title: METRICS PERFORMANCE COMPARISON FOR COLOR IMAGE DATABASE

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In this paper, we exploit a new database of distorted test images TID2008 for verification of full-reference metrics of image visual quality. A comparative analysis of TID20008 and its nearest analog LIVE Database is presented. For a wide variety of known metrics, their correspondence to human visual system is evaluated. The values of rank correlations of Spearman and Kendall with the considered metrics and Mean Opinion Score (MOS) obtained by exploiting TID2008 in experiments are presented. The metrics are verified for both full set of distorted test images in TID2008 (1700 distorted images, 17 types of distortions) and for particular subsets of TID2008 that include distortions most important for digital image processing applications.
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paper_title: Most apparent distortion: full-reference image quality assessment and the role of strategy

paper_content:

The mainstream approach to image quality assessment has centered around accurately modeling the single most relevant strategy employed by the human visual system (HVS) when judging image quality (e.g., detecting visible differences, and extracting image structure/information). In this work, we suggest that a single strategy may not be sufficient; rather, we advocate that the HVS uses multiple strategies to determine image quality. For images containing near-threshold distortions, the image is most apparent, and thus the HVS attempts to look past the image and look for the distortions (a detection-based strategy). For images containing clearly visible distortions, the distortions are most apparent, and thus the HVS attempts to look past the distortion and look for the image's subject matter (an appearance-based strategy). Here, we present a quality assessment method [most apparent distortion (MAD)], which attempts to explicitly model these two separate strategies. Local luminance and contrast masking are used to estimate detection-based perceived distortion in high-quality images, whereas changes in the local statistics of spatial-frequency components are used to estimate appearance-based perceived distortion in low-quality images. We show that a combination of these two measures can perform well in predicting subjective ratings of image quality.
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paper_title: Effective and Efficient Blind Quality Evaluator for Contrast Distorted Images

paper_content:

This paper mainly focuses on developing a blind quality assessment method that can effectively and efficiently evaluate the quality of contrast distorted images without requiring reference information. Through experiments, we discover and validate that the global intensity change is the main characteristic of contrast distorted images and has a close relationship to the perceptual quality. With these observations, two elements are utilized to quantify this characteristic, i.e., the maximum information entropy of intensity values and the Kullback–Leibler (K–L) divergence between the test image’s intensity histogram and the prior one based on the statistical experiment over a great number of high-quality images. To be specific, the entropy represents the valuable information of an image and the K–L divergence reflects the change degree of intensity distribution. In view of these, the proposed method is generated by combining these two elements linearly. Extensive experiments on three publicly available databases demonstrate the superiority of the proposed method. More specifically, it is more consistent with subjective evaluation results than the state-of-the-art image quality assessment methods and requires a lower computational complexity.
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paper_title: Reduced-reference image quality assessment using a wavelet-domain natural image statistic model

paper_content:

Reduced-reference (RR) image quality measures aim to predict the visual quality of distorted images with only partial information about the reference images. In this paper, we propose an RR quality assessment method based on a natural image statistic model in the wavelet transform domain. In particular, we observe that the marginal distribution of wavelet coefficients changes in different ways for different types of image distortions. To quantify such changes, we estimate the Kullback-Leibler distance between the marginal distributions of wavelet coefficients of the reference and distorted images. A generalized Gaussian model is employed to summarize the marginal distribution of wavelet coefficients of the reference image, so that only a relatively small number of RR features are needed for the evaluation of image quality. The proposed method is easy to implement and computationally efficient. In addition, we find that many well-known types of image distortion lead to significant changes in wavelet coefficient histograms, and thus are readily detectable by our measure. The algorithm is tested with subjective ratings of a large image database that contains images corrupted with a wide variety of distortion types.
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paper_title: Reduced-reference metric design for objective perceptual quality assessment in wireless imaging

paper_content:

The rapid growth of third and development of future generation mobile systems has led to an increase in the demand for image and video services. However, the hostile nature of the wireless channel makes the deployment of such services much more challenging, as in the case of a wireline system. In this context, the importance of taking care of user satisfaction with service provisioning as a whole has been recognized. The related user-oriented quality concepts cover end-to-end quality of service and subjective factors such as experiences with the service. To monitor quality and adapt system resources, performance indicators that represent service integrity have to be selected and related to objective measures that correlate well with the quality as perceived by humans. Such objective perceptual quality metrics can then be utilized to optimize quality perception associated with applications in technical systems. In this paper, we focus on the design of reduced-reference objective perceptual image quality metrics for use in wireless imaging. Specifically, the normalized hybrid image quality metric (NHIQM) and a perceptual relevance weighted L"p-norm are designed. The main idea behind both feature-based metrics relates to the fact that the human visual system (HVS) is trained to extract structural information from the viewing area. Accordingly, NHIQM and L"p-norm are designed to account for different structural artifacts that have been observed in our distortion model of a wireless link. The extent by which individual artifacts are present in a given image is obtained by measuring related image features. The overall quality measure is then computed as a weighting sum of the features with the respective perceptual relevance weight obtained from subjective experiments. The proposed metrics differ mainly in the pooling of the features and amount of reduced-reference produced. While NHIQM performs the pooling at the transmitter of the system to produce a single value as reduced-reference, the L"p-norm requires all involved feature values from the transmitted and received image to perform the pooling on the feature differences at the receiver. In addition, non-linear mapping functions are developed that relate the metric values to predicted mean opinion scores (MOS) and account for saturations in the HVS. The evaluation of prediction performance of NHIQM and the L"p-norm reveals their excellent correlation with human perception in terms of accuracy, monotonicity, and consistency. This holds not only for the prediction performance on images taken for the training of the metrics but also for the generalization to unknown images. In addition, it is shown that the NHIQM approach and the perceptual relevance weighted L"p-norm outperform other prominent objective quality metrics in prediction performance.
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paper_title: On the Subjective Assessment of the Perceived Quality of Medical Images and Videos

paper_content:

Medical professionals are viewing an increasing number of images and videos in their clinical routine. However, various types of distortions can affect medical imaging data, and therefore impact the viewers' experienced quality and their clinical practice. Thus it is necessary to quantify this impact and understand how the viewers, i.e., medical experts, perceive the quality of (distorted) images and videos. In this paper, we present an up-to-date review of the methodologies used in the literature for the subjective quality assessment of medical images and videos and discuss their merits and drawbacks depending on the use case.
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paper_title: Perceptual Quality Assessment of Screen Content Images

paper_content:

Research on screen content images (SCIs) becomes important as they are increasingly used in multi-device communication applications. In this paper, we present a study on perceptual quality assessment of distorted SCIs subjectively and objectively. We construct a large-scale screen image quality assessment database (SIQAD) consisting of 20 source and 980 distorted SCIs. In order to get the subjective quality scores and investigate, which part (text or picture) contributes more to the overall visual quality, the single stimulus methodology with 11 point numerical scale is employed to obtain three kinds of subjective scores corresponding to the entire, textual, and pictorial regions, respectively. According to the analysis of subjective data, we propose a weighting strategy to account for the correlation among these three kinds of subjective scores. Furthermore, we design an objective metric to measure the visual quality of distorted SCIs by considering the visual difference of textual and pictorial regions. The experimental results demonstrate that the proposed SCI perceptual quality assessment scheme, consisting of the objective metric and the weighting strategy, can achieve better performance than 11 state-of-the-art IQA methods. To the best of our knowledge, the SIQAD is the first large-scale database published for quality evaluation of SCIs, and this research is the first attempt to explore the perceptual quality assessment of distorted SCIs.
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paper_title: FSIM: A Feature Similarity Index for Image Quality Assessment

paper_content:

Image quality assessment (IQA) aims to use computational models to measure the image quality consistently with subjective evaluations. The well-known structural similarity index brings IQA from pixel- to structure-based stage. In this paper, a novel feature similarity (FSIM) index for full reference IQA is proposed based on the fact that human visual system (HVS) understands an image mainly according to its low-level features. Specifically, the phase congruency (PC), which is a dimensionless measure of the significance of a local structure, is used as the primary feature in FSIM. Considering that PC is contrast invariant while the contrast information does affect HVS' perception of image quality, the image gradient magnitude (GM) is employed as the secondary feature in FSIM. PC and GM play complementary roles in characterizing the image local quality. After obtaining the local quality map, we use PC again as a weighting function to derive a single quality score. Extensive experiments performed on six benchmark IQA databases demonstrate that FSIM can achieve much higher consistency with the subjective evaluations than state-of-the-art IQA metrics.
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paper_title: Reduced-Reference Image Quality Assessment Using Divisive Normalization-Based Image Representation

paper_content:

Reduced-reference image quality assessment (RRIQA) methods estimate image quality degradations with partial information about the ldquoperfect-qualityrdquo reference image. In this paper, we propose an RRIQA algorithm based on a divisive normalization image representation. Divisive normalization has been recognized as a successful approach to model the perceptual sensitivity of biological vision. It also provides a useful image representation that significantly improves statistical independence for natural images. By using a Gaussian scale mixture statistical model of image wavelet coefficients, we compute a divisive normalization transformation (DNT) for images and evaluate the quality of a distorted image by comparing a set of reduced-reference statistical features extracted from DNT-domain representations of the reference and distorted images, respectively. This leads to a generic or general-purpose RRIQA method, in which no assumption is made about the types of distortions occurring in the image being evaluated. The proposed algorithm is cross-validated using two publicly-accessible subject-rated image databases (the UT-Austin LIVE database and the Cornell-VCL A57 database) and demonstrates good performance across a wide range of image distortions.
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paper_title: Making a “Completely Blind” Image Quality Analyzer

paper_content:

An important aim of research on the blind image quality assessment (IQA) problem is to devise perceptual models that can predict the quality of distorted images with as little prior knowledge of the images or their distortions as possible. Current state-of-the-art “general purpose” no reference (NR) IQA algorithms require knowledge about anticipated distortions in the form of training examples and corresponding human opinion scores. However we have recently derived a blind IQA model that only makes use of measurable deviations from statistical regularities observed in natural images, without training on human-rated distorted images, and, indeed without any exposure to distorted images. Thus, it is “completely blind.” The new IQA model, which we call the Natural Image Quality Evaluator (NIQE) is based on the construction of a “quality aware” collection of statistical features based on a simple and successful space domain natural scene statistic (NSS) model. These features are derived from a corpus of natural, undistorted images. Experimental results show that the new index delivers performance comparable to top performing NR IQA models that require training on large databases of human opinions of distorted images. A software release is available at http://live.ece.utexas.edu/research/quality/niqe_release.zip.
---
paper_title: Reduced-reference image quality assessment based on statistics of edge patterns

paper_content:

Recently, research of Objective Image Quality Assessment (IQA) has gained much attention due to its wide application ::: prospect. Among them, the Reduced-Reference (RR) methods estimate perceptual quality of distorted images with ::: partial information from the reference images. This paper proposes a novel universal RR-IQA metric based on the ::: statistics of edge patterns. Firstly, the binary edge maps of the reference and distorted images are created by the LOG ::: operator and zero-crossing detection. Based on them, 15 groups of typical edge patterns are extracted and then their ::: statistical distributions are calculated respectively for the reference and distortion images. The proposed RR-IQA metric ::: is achieved by computing the L-1 Minkowski distance between those two distributions. We have evaluated this metric on ::: six publicly accessible subjective IQA databases. Experiments shows that the proposed metric featured with typical edge ::: patterns outperform other methods in terms of data volume, accuracy and consistency with human perception. In a way, ::: our work provides a new view to the IQA metric design.
---
paper_title: Unsupervised feature learning framework for no-reference image quality assessment

paper_content:

In this paper, we present an efficient general-purpose objective no-reference (NR) image quality assessment (IQA) framework based on unsupervised feature learning. The goal is to build a computational model to automatically predict human perceived image quality without a reference image and without knowing the distortion present in the image. Previous approaches for this problem typically rely on hand-crafted features which are carefully designed based on prior knowledge. In contrast, we use raw-image-patches extracted from a set of unlabeled images to learn a dictionary in an unsupervised manner. We use soft-assignment coding with max pooling to obtain effective image representations for quality estimation. The proposed algorithm is very computationally appealing, using raw image patches as local descriptors and using soft-assignment for encoding. Furthermore, unlike previous methods, our unsupervised feature learning strategy enables our method to adapt to different domains. CORNIA (Codebook Representation for No-Reference Image Assessment) is tested on LIVE database and shown to perform statistically better than the full-reference quality measure, structural similarity index (SSIM) and is shown to be comparable to state-of-the-art general purpose NR-IQA algorithms.
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paper_title: Combining Local and Global Measures for DIBR-Synthesized Image Quality Evaluation

paper_content:

Depth-image-based-rendering (DIBR) techniques are significant for 3D video applications, e.g., 3D television and free viewpoint video (FVV). Unfortunately, the DIBR-synthesized image suffers from various distortions, which induce an annoying viewing experience for the entire FVV. Proposing a quality evaluator for DIBR-synthesized images is fundamental for the design of perceptual friendly FVV systems. Since the associated reference image is usually not accessible, full-reference (FR) methods cannot be directly applied for quality evaluation of the synthesized image. In addition, most traditional no-reference (NR) methods fail to effectively measure the specifically DIBR-related distortions. In this paper, we propose a novel NR quality evaluation method accounting for two categories of DIBR-related distortions, i.e., geometric distortions and sharpness. First, the disoccluded regions, as one of the most obvious geometric distortions, are captured by analyzing local similarity. Then, another typical geometric distortion (i.e., stretching) is detected and measured by calculating the similarity between it and its equal-size adjacent region. Second, considering the property of scale invariance, the global sharpness is measured as the distance between the distorted image and its downsampled version. Finally, the perceptual quality is estimated by linearly pooling the scores of two geometric distortions and sharpness together. Experimental results verify the superiority of the proposed method over the prevailing FR and NR metrics. More specifically, it is superior to all competing methods except APT in terms of effectiveness, but greatly outmatches APT in terms of implementation time.
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paper_title: The statistics of natural images

paper_content:

Recently there has been a resurgence of interest in the properties of natural images. Their statistics are important not only in image compression but also for the study of sensory processing in biology, which can be viewed as satisfying certain ‘design criteria’. This review summarizes previous work on image statistics and presents our own data. Perhaps the most notable property of natural images is an invariance to scale. We present data to support this claim as well as evidence for a hierarchical invariance in natural scenes. These symmetries provide a powerful description of natural images as they greatly restrict the class of allowed distributions.
---
paper_title: Making a “Completely Blind” Image Quality Analyzer

paper_content:

An important aim of research on the blind image quality assessment (IQA) problem is to devise perceptual models that can predict the quality of distorted images with as little prior knowledge of the images or their distortions as possible. Current state-of-the-art “general purpose” no reference (NR) IQA algorithms require knowledge about anticipated distortions in the form of training examples and corresponding human opinion scores. However we have recently derived a blind IQA model that only makes use of measurable deviations from statistical regularities observed in natural images, without training on human-rated distorted images, and, indeed without any exposure to distorted images. Thus, it is “completely blind.” The new IQA model, which we call the Natural Image Quality Evaluator (NIQE) is based on the construction of a “quality aware” collection of statistical features based on a simple and successful space domain natural scene statistic (NSS) model. These features are derived from a corpus of natural, undistorted images. Experimental results show that the new index delivers performance comparable to top performing NR IQA models that require training on large databases of human opinions of distorted images. A software release is available at http://live.ece.utexas.edu/research/quality/niqe_release.zip.
---
paper_title: Retinal image quality assessment using generic image quality indicators

paper_content:

A retinal image gradability assessment algorithm based on the fusion of generic image quality indicators is introduced. Four features quantifying image colour, focus, contrast and illumination are computed using novel image processing techniques. These quality indicators are also combined and classified to evaluate the image suitability for diagnostic purposes. The algorithm performance is thoroughly appraised through comparison of the automatic classification results of 2032 retinal images from proprietary, DRIVE, Messidor, ROC and STARE datasets with human made classification, revealing a sensitivity of 99.76% and a specificity of 99.49%. The algorithm computational complexity and sensitivity to image noise and resolution were also experimentally quantified demonstrating very good performance and confirming the usability of the solution in an ambulatory application environment.
---
paper_title: Identification of suitable fundus images using automated quality assessment methods

paper_content:

Retinal image quality assessment (IQA) is a crucial process for automated retinal image analysis systems to obtain an accurate and successful diagnosis of retinal diseases. Consequently, the first step in a good retinal image analysis system is measuring the quality of the input image. We present an approach for finding medically suitable retinal images for retinal diagnosis. We used a three-class grading system that consists of good, bad, and outlier classes. We created a retinal image quality dataset with a total of 216 consecutive images called the Diabetic Retinopathy Image Database. We identified the suitable images within the good images for automatic retinal image analysis systems using a novel method. Subsequently, we evaluated our retinal image suitability approach using the Digital Retinal Images for Vessel Extraction and Standard Diabetic Retinopathy Database Calibration level 1 public datasets. The results were measured through the F1 metric, which is a harmonic mean of precision and recall metrics. The highest F1 scores of the IQA tests were 99.60%, 96.50%, and 85.00% for good, bad, and outlier classes, respectively. Additionally, the accuracy of our suitable image detection approach was 98.08%. Our approach can be integrated into any automatic retinal analysis system with sufficient performance scores.
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paper_title: Automatic retinal image quality assessment and enhancement

paper_content:

This paper describes a method for machine (computer) assessment of the quality of a retinal image. The method provides an over-all quantitative and objective measure using a quality index Q. The Q of a retinal image is calculated by the convolution of a template intensity histogram obtained from a set of typically good retinal images and the intensity histogram of the retinal image. After normalization, the Q has a maximum value of 1, indicating excellent quality, and a minimum value of 0, indicating bad quality. The paper also presents several application examples of Q in image enhancement. It is shown that the use of Q can help computer scientists evaluate the suitability and effectiveness of image enhancement methods, both quantitatively and objectively. It can further help computer scientists improve retinal image quality on a more scientific basis. Additionally, this machine image quality measure can also help physicians make medical diagnosis with more certainty and higher accuracy. Finally, it should be noted that although retinal images are used in this study, the methodology is applicable to the image quality assessment and enhancement of other types of medical images.
---
paper_title: Deep CNN-Based Blind Image Quality Predictor

paper_content:

Image recognition based on convolutional neural networks (CNNs) has recently been shown to deliver the state-of-the-art performance in various areas of computer vision and image processing. Nevertheless, applying a deep CNN to no-reference image quality assessment (NR-IQA) remains a challenging task due to critical obstacles, i.e., the lack of a training database. In this paper, we propose a CNN-based NR-IQA framework that can effectively solve this problem. The proposed method—deep image quality assessor (DIQA)—separates the training of NR-IQA into two stages: 1) an objective distortion part and 2) a human visual system-related part. In the first stage, the CNN learns to predict the objective error map, and then the model learns to predict subjective score in the second stage. To complement the inaccuracy of the objective error map prediction on the homogeneous region, we also propose a reliability map. Two simple handcrafted features were additionally employed to further enhance the accuracy. In addition, we propose a way to visualize perceptual error maps to analyze what was learned by the deep CNN model. In the experiments, the DIQA yielded the state-of-the-art accuracy on the various databases.
---
paper_title: Deep Neural Networks for No-Reference and Full-Reference Image Quality Assessment

paper_content:

We present a deep neural network-based approach to image quality assessment (IQA). The network is trained end-to-end and comprises ten convolutional layers and five pooling layers for feature extraction, and two fully connected layers for regression, which makes it significantly deeper than related IQA models. Unique features of the proposed architecture are that: 1) with slight adaptations it can be used in a no-reference (NR) as well as in a full-reference (FR) IQA setting and 2) it allows for joint learning of local quality and local weights, i.e., relative importance of local quality to the global quality estimate, in an unified framework. Our approach is purely data-driven and does not rely on hand-crafted features or other types of prior domain knowledge about the human visual system or image statistics. We evaluate the proposed approach on the LIVE, CISQ, and TID2013 databases as well as the LIVE In the wild image quality challenge database and show superior performance to state-of-the-art NR and FR IQA methods. Finally, cross-database evaluation shows a high ability to generalize between different databases, indicating a high robustness of the learned features.
---
paper_title: Fully Deep Blind Image Quality Predictor

paper_content:

In general, owing to the benefits obtained from original information, full-reference image quality assessment (FR-IQA) achieves relatively higher prediction accuracy than no-reference image quality assessment (NR-IQA). By fully utilizing reference images, conventional FR-IQA methods have been investigated to produce objective scores that are close to subjective scores. In contrast, NR-IQA does not consider reference images; thus, its performance is inferior to that of FR-IQA. To alleviate this accuracy discrepancy between FR-IQA and NR-IQA methods, we propose a blind image evaluator based on a convolutional neural network (BIECON). To imitate FR-IQA behavior, we adopt the strong representation power of a deep convolutional neural network to generate a local quality map, similar to FR-IQA. To obtain the best results from the deep neural network, replacing hand-crafted features with automatically learned features is necessary. To apply the deep model to the NR-IQA framework, three critical problems must be resolved: 1) lack of training data; 2) absence of local ground truth targets; and 3) different purposes of feature learning. BIECON follows the FR-IQA behavior using the local quality maps as intermediate targets for conventional neural networks, which leads to NR-IQA prediction accuracy that is comparable with that of state-of-the-art FR-IQA methods.
---
paper_title: Blind Image Quality Assessment via Deep Learning

paper_content:

This paper investigates how to blindly evaluate the visual quality of an image by learning rules from linguistic descriptions. Extensive psychological evidence shows that humans prefer to conduct evaluations qualitatively rather than numerically. The qualitative evaluations are then converted into the numerical scores to fairly benchmark objective image quality assessment (IQA) metrics. Recently, lots of learning-based IQA models are proposed by analyzing the mapping from the images to numerical ratings. However, the learnt mapping can hardly be accurate enough because some information has been lost in such an irreversible conversion from the linguistic descriptions to numerical scores. In this paper, we propose a blind IQA model, which learns qualitative evaluations directly and outputs numerical scores for general utilization and fair comparison. Images are represented by natural scene statistics features. A discriminative deep model is trained to classify the features into five grades, corresponding to five explicit mental concepts, i.e., excellent, good, fair, poor, and bad. A newly designed quality pooling is then applied to convert the qualitative labels into scores. The classification framework is not only much more natural than the regression-based models, but also robust to the small sample size problem. Thorough experiments are conducted on popular databases to verify the model’s effectiveness, efficiency, and robustness.
---
paper_title: Convolutional Neural Networks for No-Reference Image Quality Assessment

paper_content:

In this work we describe a Convolutional Neural Network (CNN) to accurately predict image quality without a reference image. Taking image patches as input, the CNN works in the spatial domain without using hand-crafted features that are employed by most previous methods. The network consists of one convolutional layer with max and min pooling, two fully connected layers and an output node. Within the network structure, feature learning and regression are integrated into one optimization process, which leads to a more effective model for estimating image quality. This approach achieves state of the art performance on the LIVE dataset and shows excellent generalization ability in cross dataset experiments. Further experiments on images with local distortions demonstrate the local quality estimation ability of our CNN, which is rarely reported in previous literature.
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paper_title: A deep neural network for image quality assessment

paper_content:

This paper presents a no reference image (NR) quality assessment (IQA) method based on a deep convolutional neural network (CNN). The CNN takes unpreprocessed image patches as an input and estimates the quality without employing any domain knowledge. By that, features and natural scene statistics are learnt purely data driven and combined with pooling and regression in one framework. We evaluate the network on the LIVE database and achieve a linear Pearson correlation superior to state-of-the-art NR IQA methods. We also apply the network to the image forensics task of decoder-sided quantization parameter estimation and also here achieve correlations of r = 0.989.
---
paper_title: A deep learning approach to document image quality assessment

paper_content:

This paper proposes a deep learning approach for document image quality assessment. Given a noise corrupted document image, we estimate its quality score as a prediction of OCR accuracy. First the document image is divided into patches and non-informative patches are sifted out using Otsu's binarization technique. Second, quality scores are obtained for all selected patches using a Convolutional Neural Network (CNN), and the patch scores are averaged over the image to obtain the document score. The proposed CNN contains two layers of convolution, location blind max-min pooling, and Rectified Linear Units in the fully connected layers. Experiments on two document quality datasets show our method achieved the state of the art performance.
---
paper_title: Visual Importance and Distortion Guided Deep Image Quality Assessment Framework

paper_content:

In this paper, we tackle the problem of no-reference image quality assessment (IQA). A learning-based IQA framework “VIDGIQA” is proposed, which extracts quality features from the input image and regresses the visual quality on these features. Since different distortions lead to different visual perceptions in the human visual system, distortion information is adopted to guide the feature learning process together with the human quality scores. Besides, a regression method is proposed to model and estimate the visual importance weights of all local regions, which can effectively improve the performance. More importantly, all these operations are integrated into one deep neural network, so that they can be jointly optimized and well cooperate with each other. Experiments were conducted to demonstrate the power of the proposed method on several datasets, including the LIVE dataset [1] , the TID 2013 dataset [2] , the LIVE multiply distorted IQA dataset [3] , CSIQ [4] , and the LIVE in the wild image quality database [5] . The proposed method achieves 0.969 and 0.973 on the LIVE dataset [1] in terms of the spearman rank-order correlation coefficient and the Pearson linear correlation coefficient, respectively, which outperforms the state-of-the-art methods.
---
paper_title: Deep learning network for blind image quality assessment

paper_content:

Nowadays, blind image quality assessment (BIQA) has been intensively studied with machine learning, such as support vector machine (SVM) and k-means. Existing BIQA metrics, however, do not perform robust for various kinds of distortion types. We believe this problem is because those frequently used traditional machine learning techniques exploit shallow architectures, which only contain one single layer of nonlinear feature transformation, and thus cannot highly mimic the mechanism of human visual perception to image quality. The recent advance of deep neural network (DNN) can help to solve this problem, since the DNN is found to better capture the essential attributes of images. We in this paper therefore introduce a new Deep learning based Image Quality Index (DIQI) for blind quality assessment. Extensive studies are conducted on the new TID2013 database and confirm the effectiveness of our DIQI relative to classical full-reference and state-of-the-art reduced- and no-reference IQA approaches.
---
paper_title: Automatic retinal image quality assessment and enhancement

paper_content:

This paper describes a method for machine (computer) assessment of the quality of a retinal image. The method provides an over-all quantitative and objective measure using a quality index Q. The Q of a retinal image is calculated by the convolution of a template intensity histogram obtained from a set of typically good retinal images and the intensity histogram of the retinal image. After normalization, the Q has a maximum value of 1, indicating excellent quality, and a minimum value of 0, indicating bad quality. The paper also presents several application examples of Q in image enhancement. It is shown that the use of Q can help computer scientists evaluate the suitability and effectiveness of image enhancement methods, both quantitatively and objectively. It can further help computer scientists improve retinal image quality on a more scientific basis. Additionally, this machine image quality measure can also help physicians make medical diagnosis with more certainty and higher accuracy. Finally, it should be noted that although retinal images are used in this study, the methodology is applicable to the image quality assessment and enhancement of other types of medical images.
---
paper_title: Automated assessment of diabetic retinal image quality based on clarity and field definition.

paper_content:

PURPOSE ::: To evaluate the performance of an automated retinal image quality assessment system for use in automated diabetic retinopathy grading. ::: ::: ::: METHODS ::: Algorithmic methods have been developed for assessing the quality of 45 degrees single field retinal images for use in diabetic retinopathy screening. For this purpose, image quality was defined by two aspects: image clarity and field definition. An image with adequate clarity was defined as one that shows sufficient detail for automated retinopathy grading. The visibility of the macular vessels was used as an indicator of image clarity, since these vessels are known to be narrow and become less visible with any image degradation. An image with adequate field definition was defined as one that shows the desired field of view for retinopathy grading, including the full 45 degrees field of view, the optic disc, and at least two optic disc diameters of visible retina around the fovea. From 489 patients attending a diabetic retinopathy screening program, 1039 retinal images were obtained. The images were graded by a clinician for image clarity and field definition, with a comprehensive image-quality grading scheme. ::: ::: ::: RESULTS ::: The sensitivity and specificity were, respectively, 100% and 90.9% for inadequate clarity detection, 95.3% and 96.4% for inadequate field definition detection, and 99.1% and 89.4% for inadequate overall quality detection. ::: ::: ::: CONCLUSIONS ::: The automated system performs with sufficient accuracy to form part of an automated diabetic retinopathy grading system.
---
paper_title: Automated localisation of the optic disc, fovea, and retinal blood vessels from digital colour fundus images

paper_content:

Aim—To recognise automatically the main components of the fundus on digital colour images. Methods—The main features of a fundus retinal image were defined as the optic disc, fovea, and blood vessels. Methods are described for their automatic recognition and location. 112 retinal images were preprocessed via adaptive, local, contrast enhancement. The optic discs were located by identifying the area with the highest variation in intensity of adjacent pixels. Blood vessels were identified by means of a multilayer perceptron neural net, for which the inputs were derived from a principal component analysis (PCA) of the image and edge detection of the first component of PCA. The foveas were identified using matching correlation together with characteristics typical of a fovea—for example, darkest area in the neighbourhood of the optic disc. The main components of the image were identified by an experienced ophthalmologist for comparison with computerised methods. Results—The sensitivity and specificity of the recognition of each retinal main component was as follows:99.1% and 99.1% for the optic disc; 83.3% and 91.0% for blood vessels; 80.4% and 99.1% for the fovea. Conclusions—In this study the optic disc, blood vessels, and fovea were accurately detected. The identification of the normal components of the retinal image will aid the future detection of diseases in these regions. In diabetic retinopathy, for example,an image could be analysed for retinopathy with reference to sight threatening complications such as disc neovascularisation, vascular changes, or foveal exudation. (Br J Ophthalmol 1999;83:902‐910)
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paper_title: Contrast measurement for no-reference retinal image quality assessment

paper_content:

Retinal fundus image provides information of retinal pathologies to diagnose some diseases by computer automatic detection. The quality of the retinal image mostly affects the detection results. In this research, blood vessels contrast measurement algorithm is approached as the first step in no-reference retinal image quality metric. The step includes segmentation of blood vessels. This work was used retinal images from HEI-MED database. The retinal images are divided as poor and good quality, and then compared with the expert assessment. The result shows that the performance of the approach algorithm is correlated closely with the expert assessment. The qualitative evaluation achieves sensitivity 0.97619, specificity 0.8 and accuracy 0.89362.
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paper_title: Tram-Line filtering for retinal vessel segmentation

paper_content:

The segmentation of the vascular network from retinal fundal images is a fundamental step in the analysis of the retina, and may be used for a number of purposes, including diagnosis of diabetic retinopathy. However, due to the variability of retinal images segmentation is difficult, particularly with images of diseased retina which include significant distractors. ::: This paper introduces a non-linear filter for vascular segmentation, which is particularly robust against such distractors. We demonstrate results on the publicly-available STARE dataset, superior to Stare’s performance, with 57.2% of the vascular network (by length) successfully located, with 97.2% positive predictive value measured by vessel length, compared with 57% and 92.2% for Stare. The filter is also simple and computationally efficient.
---
paper_title: Exudate-based diabetic macular edema detection in fundus images using publicly available datasets

paper_content:

Diabetic macular edema (DME) is a common vision threatening complication of diabetic retinopathy. In a large scale screening environment DME can be assessed by detecting exudates (a type of bright lesions) in fundus images. In this work, we introduce a new methodology for diagnosis of DME using a novel set of features based on colour, wavelet decomposition and automatic lesion segmentation. These features are employed to train a classifier able to automatically diagnose DME through the presence of exudation. We present a new publicly available dataset with ground-truth data containing 169 patients from various ethnic groups and levels of DME. This and other two publicly available datasets are employed to evaluate our algorithm. We are able to achieve diagnosis performance comparable to retina experts on the MESSIDOR (an independently labelled dataset with 1200 images) with cross-dataset testing (e.g., the classifier was trained on an independent dataset and tested on MESSIDOR). Our algorithm obtained an AUC between 0.88 and 0.94 depending on the dataset/features used. Additionally, it does not need ground truth at lesion level to reject false positives and is computationally efficient, as it generates a diagnosis on an average of 4.4s (9.3s, considering the optic nerve localisation) per image on an 2.6 GHz platform with an unoptimised Matlab implementation.
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paper_title: An automated retinal image quality grading algorithm

paper_content:

This paper introduces an algorithm for the automated assessment of retinal fundus image quality grade. Retinal image quality grading assesses whether the quality of the image is sufficient to allow diagnostic procedures to be applied. Automated quality analysis is an important preprocessing step in algorithmic diagnosis, as it is necessary to ensure that images are sufficiently clear to allow pathologies to be visible. The algorithm is based on standard recommendations for quality analysis by human screeners, examining the clarity of retinal vessels within the macula region. An evaluation against a reference standard data-set is given; it is shown that the algorithm's performance correlates closely with that of clinicians manually grading image quality.
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paper_title: Image quality assessment: from error visibility to structural similarity

paper_content:

Objective methods for assessing perceptual image quality traditionally attempted to quantify the visibility of errors (differences) between a distorted image and a reference image using a variety of known properties of the human visual system. Under the assumption that human visual perception is highly adapted for extracting structural information from a scene, we introduce an alternative complementary framework for quality assessment based on the degradation of structural information. As a specific example of this concept, we develop a structural similarity index and demonstrate its promise through a set of intuitive examples, as well as comparison to both subjective ratings and state-of-the-art objective methods on a database of images compressed with JPEG and JPEG2000. A MATLAB implementation of the proposed algorithm is available online at http://www.cns.nyu.edu//spl sim/lcv/ssim/.
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paper_title: An improved matched filter for blood vessel detection of digital retinal images

paper_content:

The matched filter has been widely used in the detection of blood vessels of the human retina digital image. In this paper, the matched filter response to the detection of blood vessels is increased by proposing better filter parameters. These filter parameters are found by using an optimization procedure on 20 retina images of the DRIVE database. Comparisons with other approaches show that the matched filter that uses the newly found parameters outperforms the matched filter that uses the classical filter parameters as well as some vessel detection techniques. A technique is also discussed to find the best threshold value for the continuous matched filter output image and hence the best segmented vessel image.
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paper_title: Retinal Blood Vessel Extraction by Using Multi-resolution Matched Filtering and Directional Region Growing Segmentation

paper_content:

A new method to extract retinal blood vessels from a colour fundus image is described. Digital colour h d u s images are contrast enhanced in order to obtain sharp edges. The green bands are selected and transformed to correlation coefficient images by using two sets of Gaussian kernel patches of distinct scales of resolution. Blood vessels are then extracted by means of a new algorithm, directional recursive region growing segmentation or D-RRGS. The segmentation results have been compared with clinically-generated ground truth and evaluated in terms of sensitivity and specificity. The results are encouraging and will be used for fiuther application such as blood vessel diameter measurement. David usher2 , James F. Boyce Department of Physics, King's College London therefore required as a pre-processing component of an automatic diagnosislscreening system. In this study, an effective segmentation algorithm for the classification of retinal blood vessels is described. Digital fundus images are colour standardised and contrast enhanced in order to obtain sharp edges in a consistent way. The green bands of the images are then transformed to correlation coefficient images by using two sets of Gaussian kernels. A new algorithm named Directional Recursive Region Growing Segmentation or D-RRGS is applied to the correlation coefficient images to extract blood vessels. The segmentation results are compared with clinically generated vessel segmentations and assessed in terms of sensitivity and specificity of vessel identification. The results are 1. Preface encouraging and will be used in fiuther applications such as Retinal blood vessels are one of the most important blood vessel diameter measurement. components in ophthalmic diagnosis. As the network of the retinal blood vessels doesn't change very much over time, 2. Algorithm the location of bifurcation andlor end points can be used as Pre-processing feature points to identify an eye. Detecting abnormalities This process includes local contrast enhancement and green such as venous looping or beadings is critical for early band extraction of the digital fundus image. (If necessary, treatment, as they are, in most cases, indications of colour standardisation to reduce the variation of retinal potentially sight-threatening retinopathy[l]. In order to colour found in a racially heterogeneous patient sample may utilise these u se l l characteristics of retinal blood vessels, it be applied.) is very important to obtain their locations and shapes The original image is converted from the RGB colour model accurately. to HSI. The I band of the resulting image is processed by In many of the reported studies on automatic fundus image local contrast enhancement. Local contrast enhancement is a analysis and diagnosis[2][3][4] normal components within signal to noise enhancement process to create images which the image, such as blood vessels or fovea, are detected and are improved for the subsequent retinal blood vessel identified before starting abnormal component detection. detection. It emphasises the local contrast of the intensity Pathologies such micro aneurysms or haemorrhages, located values of an image so that the blood vessels are more clearly close to blood vessels, may be misclassified as blood vessels distinguished from the background. It is then converted back and removed in the pre-processing, resulting in reduced to RGB colourmodel. specificity of pathology detection and hence possible From visual observation, blood vessels generally exhibit the misdiagnosis. Accurate retinal blood vessel extraction is greatest contrast from the background in the green band and 1Address: 1 Ikegami, Owariasahi-city, Aichi, 488-8501, Japan. E-mail: mitsutoshi-himaga@itg.hitachi.co.jp 2Address: Strand, London WC2R 2LS, UK. E-mail: david.usher@kcl.ac.uk 3Address: Strand, London WC2R 2LS, UK. E-mail: james.boyce@kcl.ac.uk Fig.21: Pre-processing (a) Original image (multi-band) (b) Contrast enhanced image (multi-band) (c) Contrast enhanced image (green band) therefore the green band is selected from the contrast where enhanced images for further processing. M N 1 M N The stages of pre-processing for a typical fundus image are I , ) pp = -c c p( i , i ) (2.3) p~ = ,=, i=, NM ,=, i= l shown in Fig.21. Gaussian kernel patches The original range of t he correlation coefficient is In order to cope with a large of blood vessel widths, [-l , l l , however, it is to convert the range two sets of kernel patches were created; the first set of [092551 in order a 256-t0ne greyscale kernels (set A) is for medium to thick blood vessels and the image as Output. other set (set B) is for fine vessels. Each set consists of The conversion is linear and the equation used for the twelve kernel patches of different angle. creation of the intensity is The suitable two dimensional kernel K(x,y) may be Intk,y) = (thy)+ 1) x127.5 (2.5) mathematically described as M/2 XI W-M/2, N/2 < y < H-N/2 (2.6) where Int(x,y) is the intensity value for the output image and K(x,y)=-expe 1 2 4 for 1 y 1 L/2 (2.1) c(x,y) is the correlation coefficient at the coordinate (x,y) where a is the variance of the intensity profile, L is the where the centre point of the kernel patch matches. length of the segment for which the blood vessel is assumed to have a fixed orientation. In this study, o = 2.0 and L=9 are employed as suggested by Goldbaum et a1[5]. Correlation coefficient images creation The correlation coefficient images used in this paper are created by the following algorithm. Let I(m,n) ,P(m,n) be the input image and the kernel patch respectively, and WxH, MxN be the size of I(m,n) and P(m,n) respectively, then the comelation coefficient at (x,y) in the input image is given by
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paper_title: Automated assessment of diabetic retinal image quality based on clarity and field definition.

paper_content:

PURPOSE ::: To evaluate the performance of an automated retinal image quality assessment system for use in automated diabetic retinopathy grading. ::: ::: ::: METHODS ::: Algorithmic methods have been developed for assessing the quality of 45 degrees single field retinal images for use in diabetic retinopathy screening. For this purpose, image quality was defined by two aspects: image clarity and field definition. An image with adequate clarity was defined as one that shows sufficient detail for automated retinopathy grading. The visibility of the macular vessels was used as an indicator of image clarity, since these vessels are known to be narrow and become less visible with any image degradation. An image with adequate field definition was defined as one that shows the desired field of view for retinopathy grading, including the full 45 degrees field of view, the optic disc, and at least two optic disc diameters of visible retina around the fovea. From 489 patients attending a diabetic retinopathy screening program, 1039 retinal images were obtained. The images were graded by a clinician for image clarity and field definition, with a comprehensive image-quality grading scheme. ::: ::: ::: RESULTS ::: The sensitivity and specificity were, respectively, 100% and 90.9% for inadequate clarity detection, 95.3% and 96.4% for inadequate field definition detection, and 99.1% and 89.4% for inadequate overall quality detection. ::: ::: ::: CONCLUSIONS ::: The automated system performs with sufficient accuracy to form part of an automated diabetic retinopathy grading system.
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paper_title: Automatic Parameter Selection for Denoising Algorithms Using a No-Reference Measure of Image Content

paper_content:

Across the field of inverse problems in image and video processing, nearly all algorithms have various parameters which need to be set in order to yield good results. In practice, usually the choice of such parameters is made empirically with trial and error if no "ground-truth" reference is available. Some analytical methods such as cross-validation and Stein's unbiased risk estimate (SURE) have been successfully used to set such parameters. However, these methods tend to be strongly reliant on restrictive assumptions on the noise, and also computationally heavy. In this paper, we propose a no-reference metric Q which is based upon singular value decomposition of local image gradient matrix, and provides a quantitative measure of true image content (i.e., sharpness and contrast as manifested in visually salient geometric features such as edges,) in the presence of noise and other disturbances. This measure 1) is easy to compute, 2) reacts reasonably to both blur and random noise, and 3) works well even when the noise is not Gaussian. The proposed measure is used to automatically and effectively set the parameters of two leading image denoising algorithms. Ample simulated and real data experiments support our claims. Furthermore, tests using the TID2008 database show that this measure correlates well with subjective quality evaluations for both blur and noise distortions.
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paper_title: Automatic no-reference quality assessment for retinal fundus images using vessel segmentation

paper_content:

Fundus imaging is the most commonly used modality to collect information about the human eye background. Objective and quantitative assessment of quality for the acquired images is essential for manual, computer-aided and fully automatic diagnosis. In this paper, we present a no-reference quality metric to quantify image noise and blur and its application to fundus image quality assessment. The proposed metric takes the vessel tree visible on the retina as guidance to determine an image quality score. In our experiments, the performance of this approach is demonstrated by correlation analysis with the established full-reference metrics peak-signal-to-noise ratio (PSNR) and structural similarity (SSIM). We found a Spearman rank correlation for PSNR and SSIM of 0.89 and 0.91. For real data, our metric correlates reasonable to a human observer, indicating high agreement to human visual perception.
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paper_title: Ridge-based vessel segmentation in color images of the retina

paper_content:

A method is presented for automated segmentation of vessels in two-dimensional color images of the retina. This method can be used in computer analyses of retinal images, e.g., in automated screening for diabetic retinopathy. The system is based on extraction of image ridges, which coincide approximately with vessel centerlines. The ridges are used to compose primitives in the form of line elements. With the line elements an image is partitioned into patches by assigning each image pixel to the closest line element. Every line element constitutes a local coordinate frame for its corresponding patch. For every pixel, feature vectors are computed that make use of properties of the patches and the line elements. The feature vectors are classified using a kNN-classifier and sequential forward feature selection. The algorithm was tested on a database consisting of 40 manually labeled images. The method achieves an area under the receiver operating characteristic curve of 0.952. The method is compared with two recently published rule-based methods of Hoover et al. and Jiang et al. . The results show that our method is significantly better than the two rule-based methods (p<0.01). The accuracy of our method is 0.944 versus 0.947 for a second observer.
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paper_title: Retinal Image Quality Analysis for Automatic Diabetic Retinopathy Detection

paper_content:

Sufficient image quality is a necessary prerequisite for reliable automatic detection systems in several healthcare environments. Specifically for Diabetic Retinopathy (DR) detection, poor quality fund us makes more difficult the analysis of discontinuities that characterize lesions, as well as to generate evidence that can incorrectly diagnose the presence of anomalies. Several methods have been applied for classification of image quality and recently, have shown satisfactory results. However, most of the authors have focused only on the visibility of blood vessels through detection of blurring. Furthermore, these studies frequently only used fund us images from specific cameras which are not validated on datasets obtained from different retinographers. In this paper, we propose an approach to verify essential requirements of retinal image quality for DR screening: field definition and blur detection. The methods were developed and validated on two large, representative datasets collected by different cameras. The first dataset comprises 5,776 images and the second, 920 images. For field definition, the method yields a performance close to optimal with an area under the Receiver Operating Characteristic curve (ROC) of 96.0%. For blur detection, the method achieves an area under the ROC curve of 95.5%.
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paper_title: Fast vessel segmentation in retinal images using multi-scale enhancement and second-order local entropy

paper_content:

Retinal vasculature is one of the most important anatomical structures in digital retinal photographs. Accurate ::: segmentation of retinal blood vessels is an essential task in automated analysis of retinopathy. This paper presents a new ::: and effective vessel segmentation algorithm that features computational simplicity and fast implementation. This method ::: uses morphological pre-processing to decrease the disturbance of bright structures and lesions before vessel extraction. ::: Next, a vessel probability map is generated by computing the eigenvalues of the second derivatives of Gaussian filtered ::: image at multiple scales. Then, the second order local entropy thresholding is applied to segment the vessel map. Lastly, ::: a rule-based decision step, which measures the geometric shape difference between vessels and lesions is applied to ::: reduce false positives. The algorithm is evaluated on the low-resolution DRIVE and STARE databases and the publicly ::: available high-resolution image database from Friedrich-Alexander University Erlangen-Nuremberg (Germany). The ::: proposed method achieved comparable performance to state of the art unsupervised vessel segmentation methods with a ::: competitive faster speed on the DRIVE and STARE databases. For the high resolution fundus image database, the ::: proposed algorithm outperforms an existing approach both on performance and speed. The efficiency and robustness ::: make the blood vessel segmentation method described here suitable for broad application in automated analysis of ::: retinal images.
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paper_title: Surf: Speeded up robust features

paper_content:

In this paper, we present a novel scale- and rotation-invariant interest point detector and descriptor, coined SURF (Speeded Up Robust Features). It approximates or even outperforms previously proposed schemes with respect to repeatability, distinctiveness, and robustness, yet can be computed and compared much faster. ::: ::: This is achieved by relying on integral images for image convolutions; by building on the strengths of the leading existing detectors and descriptors (in casu, using a Hessian matrix-based measure for the detector, and a distribution-based descriptor); and by simplifying these methods to the essential. This leads to a combination of novel detection, description, and matching steps. The paper presents experimental results on a standard evaluation set, as well as on imagery obtained in the context of a real-life object recognition application. Both show SURF's strong performance.
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paper_title: Image structure clustering for image quality verification of color retina images in diabetic retinopathy screening

paper_content:

Reliable verification of image quality of retinal screening images is a prerequisite for the development of automatic screening systems for diabetic retinopathy. A system is presented that can automatically determine whether the quality of a retinal screening image is sufficient for automatic analysis. The system is based on the assumption that an image of sufficient quality should contain particular image structures according to a certain pre-defined distribution. We cluster filterbank response vectors to obtain a compact representation of the image structures found within an image. Using this compact representation together with raw histograms of the R, G, and B color planes, a statistical classifier is trained to distinguish normal from low quality images. The presented system does not require any previous segmentation of the image in contrast with previous work. The system was evaluated on a large, representative set of 1000 images obtained in a screening program. The proposed method, using different feature sets and classifiers, was compared with the ratings of a second human observer. The best system, based on a Support Vector Machine, has performance close to optimal with an area under the ROC curve of 0.9968.
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paper_title: An automated retinal image quality grading algorithm

paper_content:

This paper introduces an algorithm for the automated assessment of retinal fundus image quality grade. Retinal image quality grading assesses whether the quality of the image is sufficient to allow diagnostic procedures to be applied. Automated quality analysis is an important preprocessing step in algorithmic diagnosis, as it is necessary to ensure that images are sufficiently clear to allow pathologies to be visible. The algorithm is based on standard recommendations for quality analysis by human screeners, examining the clarity of retinal vessels within the macula region. An evaluation against a reference standard data-set is given; it is shown that the algorithm's performance correlates closely with that of clinicians manually grading image quality.
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paper_title: Segmentation of Vessel-Like Patterns Using Mathematical Morphology and Curvature Evaluation

paper_content:

This paper presents an algorithm based on mathematical morphology and curvature evaluation for the detection of vessel-like patterns in a noisy environment. Such patterns are very common in medical images. Vessel detection is interesting for the computation of parameters related to blood flow. Its tree-like geometry makes it a usable feature for registration between images that can be of a different nature. In order to define vessel-like patterns, segmentation is performed with respect to a precise model. We define a vessel as a bright pattern, piece-wise connected, and locally linear, mathematical morphology is very well adapted to this description, however other patterns fit such a morphological description. In order to differentiate vessels from analogous background patterns, a cross-curvature evaluation is performed. They are separated out as they have a specific Gaussian-like profile whose curvature varies smoothly along the vessel. The detection algorithm that derives directly from this modeling is based on four steps: (1) noise reduction; (2) linear pattern with Gaussian-like profile improvement; (3) cross-curvature evaluation; (4) linear filtering. We present its theoretical background and illustrate it on real images of various natures, then evaluate its robustness and its accuracy with respect to noise.
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paper_title: Elliptical local vessel density: A fast and robust quality metric for retinal images

paper_content:

A great effort of the research community is geared towards the creation of an automatic screening system able to promptly detect diabetic retinopathy with the use of fundus cameras. In addition, there are some documented approaches for automatically judging the image quality. We propose a new set of features independent of field of view or resolution to describe the morphology of the patient's vessels. Our initial results suggest that these features can be used to estimate the image quality in a time one order of magnitude shorter than previous techniques.
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paper_title: The blur effect: perception and estimation with a new no-reference perceptual blur metric

paper_content:

To achieve the best image quality, noise and artifacts are generally removed at the cost of a loss of details generating the blur effect. To control and quantify the emergence of the blur effect, blur metrics have already been proposed in the literature. By associating the blur effect with the edge spreading, these metrics are sensitive not only to the threshold choice to classify the edge, but also to the presence of noise which can mislead the edge detection. Based on the observation that we have difficulties to perceive differences between a blurred image and the same reblurred image, we propose a new approach which is not based on transient characteristics but on the discrimination between different levels of blur perceptible on the same picture. Using subjective tests and psychophysics functions, we validate our blur perception theory for a set of pictures which are naturally unsharp or more or less blurred through one or two-dimensional low-pass filters. Those tests show the robustness and the ability of the metric to evaluate not only the blur introduced by a restoration processing but also focal blur or motion blur. Requiring no reference and a low cost implementation, this new perceptual blur metric is applicable in a large domain from a simple metric to a means to fine-tune artifacts corrections.
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paper_title: Automated assessment of diabetic retinal image quality based on clarity and field definition.

paper_content:

PURPOSE ::: To evaluate the performance of an automated retinal image quality assessment system for use in automated diabetic retinopathy grading. ::: ::: ::: METHODS ::: Algorithmic methods have been developed for assessing the quality of 45 degrees single field retinal images for use in diabetic retinopathy screening. For this purpose, image quality was defined by two aspects: image clarity and field definition. An image with adequate clarity was defined as one that shows sufficient detail for automated retinopathy grading. The visibility of the macular vessels was used as an indicator of image clarity, since these vessels are known to be narrow and become less visible with any image degradation. An image with adequate field definition was defined as one that shows the desired field of view for retinopathy grading, including the full 45 degrees field of view, the optic disc, and at least two optic disc diameters of visible retina around the fovea. From 489 patients attending a diabetic retinopathy screening program, 1039 retinal images were obtained. The images were graded by a clinician for image clarity and field definition, with a comprehensive image-quality grading scheme. ::: ::: ::: RESULTS ::: The sensitivity and specificity were, respectively, 100% and 90.9% for inadequate clarity detection, 95.3% and 96.4% for inadequate field definition detection, and 99.1% and 89.4% for inadequate overall quality detection. ::: ::: ::: CONCLUSIONS ::: The automated system performs with sufficient accuracy to form part of an automated diabetic retinopathy grading system.
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paper_title: Automated image quality evaluation of retinal fundus photographs in diabetic retinopathy screening

paper_content:

This paper presents a system that can automatically determine whether the quality of a retinal image is sufficient for computer-based diabetic retinopathy (DR) screening. The system integrates global histogram features, textural features, and vessel density, as well as a local non-reference perceptual sharpness metric. A partial least square (PLS) classifier is trained to distinguish low quality images from normal quality images. The system was evaluated on a large, representative set of 1884 non-mydriatic retinal images from 412 subjects. An area under the ROC curve of 96% was achieved.
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paper_title: Automatic no-reference quality assessment for retinal fundus images using vessel segmentation

paper_content:

Fundus imaging is the most commonly used modality to collect information about the human eye background. Objective and quantitative assessment of quality for the acquired images is essential for manual, computer-aided and fully automatic diagnosis. In this paper, we present a no-reference quality metric to quantify image noise and blur and its application to fundus image quality assessment. The proposed metric takes the vessel tree visible on the retina as guidance to determine an image quality score. In our experiments, the performance of this approach is demonstrated by correlation analysis with the established full-reference metrics peak-signal-to-noise ratio (PSNR) and structural similarity (SSIM). We found a Spearman rank correlation for PSNR and SSIM of 0.89 and 0.91. For real data, our metric correlates reasonable to a human observer, indicating high agreement to human visual perception.
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paper_title: Automatic fundus image field detection and quality assessment

paper_content:

Fundus images are an important diagnostic tool for many retinal diseases. Sometimes the images captured are of low quality and cannot be used for diagnosis requiring repeat image acquisition. So, it is efficient to have an automatic system to assess the quality of the fundus image during the time of image capture. We have developed an automatic approach to assess the quality of the acquired fundus image based upon the inherent symmetry of retinal vessels. We approach the problem of quality assessment in two ways-individual quality assessment of a single fundus image and comprehensive quality assessment of a set of three fundus images of different fields of an eye. Our method also detects the field and side of the fundus image using the position of optic disc and the intensity information in two local windows.
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paper_title: Automated quality assessment of retinal fundus photos

paper_content:

OBJECTIVE ::: Automated, objective and fast measurement of the image quality of single retinal fundus photos to allow a stable and reliable medical evaluation. ::: ::: ::: METHODS ::: The proposed technique maps diagnosis-relevant criteria inspired by diagnosis procedures based on the advise of an eye expert to quantitative and objective features related to image quality. Independent from segmentation methods it combines global clustering with local sharpness and texture features for classification. ::: ::: ::: RESULTS ::: On a test dataset of 301 retinal fundus images we evaluated our method on a given gold standard by human observers and compared it to a state of the art approach. An area under the ROC curve of 95.3% compared to 87.2% outperformed the state of the art approach. A significant p-value of 0.019 emphasizes the statistical difference of both approaches. ::: ::: ::: CONCLUSIONS ::: The combination of local and global image statistics models the defined quality criteria and automatically produces reliable and objective results in determining the image quality of retinal fundus photos.
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paper_title: Color indexing

paper_content:

Computer vision is moving into a new era in which the aim is to develop visual skills for robots that allow them to interact with a dynamic, unconstrained environment. To achieve this aim, new kinds of vision algorithms need to be developed which run in real time and subserve the robot's goals. Two fundamental goals are determining the identity of an object with a known location, and determining the location of a known object. Color can be successfully used for both tasks. ::: This dissertation demonstrates that color histograms of multicolored objects provide a robust, efficient cue for indexing into a large database of models. It shows that color histograms are stable object representations in the presence of occlusion and over change in view, and that they can differentiate among a large number of objects. For solving the identification problem, it introduces a technique called Histogram Intersection, which matches model and image histograms and a fast incremental version of Histogram Intersection which allows real-time indexing into a large database of stored models. It demonstrates techniques for dealing with crowded scenes and with models with similar color signatures. For solving the location problem it introduces an algorithm called Histogram Backprojection which performs this task efficiently in crowded scenes.
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paper_title: Human Visual System-Based Fundus Image Quality Assessment of Portable Fundus Camera Photographs

paper_content:

Telemedicine and the medical “big data” era in ophthalmology highlight the use of non-mydriatic ocular fundus photography, which has given rise to indispensable applications of portable fundus cameras. However, in the case of portable fundus photography, non-mydriatic image quality is more vulnerable to distortions, such as uneven illumination, color distortion, blur, and low contrast. Such distortions are called generic quality distortions. This paper proposes an algorithm capable of selecting images of fair generic quality that would be especially useful to assist inexperienced individuals in collecting meaningful and interpretable data with consistency. The algorithm is based on three characteristics of the human visual system—multi-channel sensation, just noticeable blur, and the contrast sensitivity function to detect illumination and color distortion, blur, and low contrast distortion, respectively. A total of 536 retinal images, 280 from proprietary databases and 256 from public databases, were graded independently by one senior and two junior ophthalmologists, such that three partial measures of quality and generic overall quality were classified into two categories. Binary classification was implemented by the support vector machine and the decision tree, and receiver operating characteristic (ROC) curves were obtained and plotted to analyze the performance of the proposed algorithm. The experimental results revealed that the generic overall quality classification achieved a sensitivity of 87.45% at a specificity of 91.66%, with an area under the ROC curve of 0.9452, indicating the value of applying the algorithm, which is based on the human vision system, to assess the image quality of non-mydriatic photography, especially for low-cost ophthalmological telemedicine applications.
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paper_title: Quality evaluation of digital fundus images through combined measures

paper_content:

The evaluation of image quality is an important step before an automatic analysis of retinal images. Several conditions can impair the acquisition of a good image, and minimum image quality requirements should be present to ensure that an automatic or semiautomatic system provides an accurate diagnosis. A method to classify fundus images as low or good quality is presented. The method starts with the detection of regions of uneven illumination and evaluates if the segmented noise masks affect a clinically relevant area (around the macula). Afterwards, focus is evaluated through a fuzzy classifier. An input vector is created extracting three focus features. The system was validated in a large dataset (1454 fundus images), obtained from an online database and an eye clinic and compared with the ratings of three observers. The system performance was close to optimal with an area under the receiver operating characteristic curve of 0.9943.
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paper_title: Identification of suitable fundus images using automated quality assessment methods

paper_content:

Retinal image quality assessment (IQA) is a crucial process for automated retinal image analysis systems to obtain an accurate and successful diagnosis of retinal diseases. Consequently, the first step in a good retinal image analysis system is measuring the quality of the input image. We present an approach for finding medically suitable retinal images for retinal diagnosis. We used a three-class grading system that consists of good, bad, and outlier classes. We created a retinal image quality dataset with a total of 216 consecutive images called the Diabetic Retinopathy Image Database. We identified the suitable images within the good images for automatic retinal image analysis systems using a novel method. Subsequently, we evaluated our retinal image suitability approach using the Digital Retinal Images for Vessel Extraction and Standard Diabetic Retinopathy Database Calibration level 1 public datasets. The results were measured through the F1 metric, which is a harmonic mean of precision and recall metrics. The highest F1 scores of the IQA tests were 99.60%, 96.50%, and 85.00% for good, bad, and outlier classes, respectively. Additionally, the accuracy of our suitable image detection approach was 98.08%. Our approach can be integrated into any automatic retinal analysis system with sufficient performance scores.
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paper_title: Automated Quality Assessment of Fundus Images via Analysis of Illumination, Naturalness and Structure

paper_content:

In remote medical diagnosis, the percentage of poor-quality fundus images is very high, which requires automated quality assessment of fundus images in the acquisition stage to reduce the retransmission cost. In this paper, we propose a fundus image quality classifier via the analysis of illumination, naturalness, and structure, which use three effective secondary indices (or 5-D feature set) and different classification methods to determine the recommendation indexes of fundus images for further diagnosis. We construct a fundus image database including ‘accept’ and ‘reject’ classes based on the definition of illumination, naturalness, and structure. The model can achieve a sensitivity of 94.69%, specificity of 92.29%, and accuracy of 93.60% for the classifying of the fundus images.
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paper_title: Generic features for fundus image quality evaluation

paper_content:

Fundus image is important for the medical screening and diagnosis of variety ophthalmopathy. The effectiveness of such a process, however, depends very much on the quality of the fundus image captured. This paper aims to asses in real-time the quality of a fundus image by first extracting a multitude of generic features, including statistical characteristics, entropy, texture, symmetry, frequency components and blur metric, which is then followed by a support vector machine (SVM) trained to filter out the poor quality image for clinic usage. The method was tested on a dataset of 3224 images collected from an eye hospital's practical screening project in rural areas in Northeastern Region of China. With the detection rate achieved being 0.9308, the corresponding false alarm rate is 0.1127, and the overall accuracy is 0.9138. The area under an ROC curve is as high as 0.9619. It is shown that the fundus images of poor quality can be automatically detected on the spot to ensure a clinically meaningful ophthalmopathy screening and diagnosis by a human expert or even an artificial intelligence software.
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paper_title: Ridge-based vessel segmentation in color images of the retina

paper_content:

A method is presented for automated segmentation of vessels in two-dimensional color images of the retina. This method can be used in computer analyses of retinal images, e.g., in automated screening for diabetic retinopathy. The system is based on extraction of image ridges, which coincide approximately with vessel centerlines. The ridges are used to compose primitives in the form of line elements. With the line elements an image is partitioned into patches by assigning each image pixel to the closest line element. Every line element constitutes a local coordinate frame for its corresponding patch. For every pixel, feature vectors are computed that make use of properties of the patches and the line elements. The feature vectors are classified using a kNN-classifier and sequential forward feature selection. The algorithm was tested on a database consisting of 40 manually labeled images. The method achieves an area under the receiver operating characteristic curve of 0.952. The method is compared with two recently published rule-based methods of Hoover et al. and Jiang et al. . The results show that our method is significantly better than the two rule-based methods (p<0.01). The accuracy of our method is 0.944 versus 0.947 for a second observer.
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paper_title: Retinal image quality assessment using generic image quality indicators

paper_content:

A retinal image gradability assessment algorithm based on the fusion of generic image quality indicators is introduced. Four features quantifying image colour, focus, contrast and illumination are computed using novel image processing techniques. These quality indicators are also combined and classified to evaluate the image suitability for diagnostic purposes. The algorithm performance is thoroughly appraised through comparison of the automatic classification results of 2032 retinal images from proprietary, DRIVE, Messidor, ROC and STARE datasets with human made classification, revealing a sensitivity of 99.76% and a specificity of 99.49%. The algorithm computational complexity and sensitivity to image noise and resolution were also experimentally quantified demonstrating very good performance and confirming the usability of the solution in an ambulatory application environment.
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paper_title: Frequency-tuned salient region detection

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Detection of visually salient image regions is useful for applications like object segmentation, adaptive compression, and object recognition. In this paper, we introduce a method for salient region detection that outputs full resolution saliency maps with well-defined boundaries of salient objects. These boundaries are preserved by retaining substantially more frequency content from the original image than other existing techniques. Our method exploits features of color and luminance, is simple to implement, and is computationally efficient. We compare our algorithm to five state-of-the-art salient region detection methods with a frequency domain analysis, ground truth, and a salient object segmentation application. Our method outperforms the five algorithms both on the ground-truth evaluation and on the segmentation task by achieving both higher precision and better recall.
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paper_title: Deep Convolutional Neural Models for Picture-Quality Prediction: Challenges and Solutions to Data-Driven Image Quality Assessment

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Convolutional neural networks (CNNs) have been shown to deliver standout performance on a wide variety of visual information processing applications. However, this rapidly developing technology has only recently been applied with systematic energy to the problem of picture-quality prediction, primarily because of limitations imposed by a lack of adequate ground-truth human subjective data. This situation has begun to change with the development of promising data-gathering methods that are driving new approaches to deep-learning-based perceptual picture-quality prediction. Here, we assay progress in this rapidly evolving field, focusing, in particular, on new ways to collect large quantities of ground-truth data and on recent CNN-based picture-quality prediction models that deliver excellent results in a large, real-world, picture-quality database.
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paper_title: Fully Deep Blind Image Quality Predictor

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In general, owing to the benefits obtained from original information, full-reference image quality assessment (FR-IQA) achieves relatively higher prediction accuracy than no-reference image quality assessment (NR-IQA). By fully utilizing reference images, conventional FR-IQA methods have been investigated to produce objective scores that are close to subjective scores. In contrast, NR-IQA does not consider reference images; thus, its performance is inferior to that of FR-IQA. To alleviate this accuracy discrepancy between FR-IQA and NR-IQA methods, we propose a blind image evaluator based on a convolutional neural network (BIECON). To imitate FR-IQA behavior, we adopt the strong representation power of a deep convolutional neural network to generate a local quality map, similar to FR-IQA. To obtain the best results from the deep neural network, replacing hand-crafted features with automatically learned features is necessary. To apply the deep model to the NR-IQA framework, three critical problems must be resolved: 1) lack of training data; 2) absence of local ground truth targets; and 3) different purposes of feature learning. BIECON follows the FR-IQA behavior using the local quality maps as intermediate targets for conventional neural networks, which leads to NR-IQA prediction accuracy that is comparable with that of state-of-the-art FR-IQA methods.
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paper_title: Convolutional Neural Networks for No-Reference Image Quality Assessment

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In this work we describe a Convolutional Neural Network (CNN) to accurately predict image quality without a reference image. Taking image patches as input, the CNN works in the spatial domain without using hand-crafted features that are employed by most previous methods. The network consists of one convolutional layer with max and min pooling, two fully connected layers and an output node. Within the network structure, feature learning and regression are integrated into one optimization process, which leads to a more effective model for estimating image quality. This approach achieves state of the art performance on the LIVE dataset and shows excellent generalization ability in cross dataset experiments. Further experiments on images with local distortions demonstrate the local quality estimation ability of our CNN, which is rarely reported in previous literature.
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paper_title: A Comprehensive Retinal Image Dataset for the Assessment of Glaucoma from the Optic Nerve Head Analysis

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Optic nerve head (ONH) segmentation problem is of interest for automated glaucoma assessment. Although various segmentation methods have been proposed in the recent past, it is difficult to evaluate and compare the performance of individual methods due to a lack of a benchmark dataset. The assessment involves segmentation of optic disk and cup region within the ONH. In this paper, we present a comprehensive dataset of retinal images of both normal and glaucomatous eyes with manual segmentations from multiple human experts. The dataset also provides expert opinion on an image representing a normal or glaucomatous eye and on the presence of notching in an image. Several state of the art methods are assessed against this dataset using cup to disc diameter ratio (CDR), area and boundary-based evaluation measures. These are presented to aid benchmarking of new methods. A supervised, notch detection method based on the segmentation results is also proposed and its assessment results are included for benchmarking.
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paper_title: Image quality classification for DR screening using deep learning.

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The quality of input images significantly affects the outcome of automated diabetic retinopathy (DR) screening systems. Unlike the previous methods that only consider simple low-level features such as hand-crafted geometric and structural features, in this paper we propose a novel method for retinal image quality classification (IQC) that performs computational algorithms imitating the working of the human visual system. The proposed algorithm combines unsupervised features from saliency map and supervised features coming from convolutional neural networks (CNN), which are fed to an SVM to automatically detect high quality vs poor quality retinal fundus images. We demonstrate the superior performance of our proposed algorithm on a large retinal fundus image dataset and the method could achieve higher accuracy than other methods. Although retinal images are used in this study, the methodology is applicable to the image quality assessment and enhancement of other types of medical images.
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paper_title: A CNN based neurobiology inspired approach for retinal image quality assessment

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Retinal image quality assessment (IQA) algorithms use different hand crafted features for training classifiers without considering the working of the human visual system (HVS) which plays an important role in IQA. We propose a convolutional neural network (CNN) based approach that determines image quality using the underlying principles behind the working of the HVS. CNNs provide a principled approach to feature learning and hence higher accuracy in decision making. Experimental results demonstrate the superior performance of our proposed algorithm over competing methods.
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paper_title: An automated retinal image quality grading algorithm

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This paper introduces an algorithm for the automated assessment of retinal fundus image quality grade. Retinal image quality grading assesses whether the quality of the image is sufficient to allow diagnostic procedures to be applied. Automated quality analysis is an important preprocessing step in algorithmic diagnosis, as it is necessary to ensure that images are sufficiently clear to allow pathologies to be visible. The algorithm is based on standard recommendations for quality analysis by human screeners, examining the clarity of retinal vessels within the macula region. An evaluation against a reference standard data-set is given; it is shown that the algorithm's performance correlates closely with that of clinicians manually grading image quality.
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paper_title: Automatic no-reference quality assessment for retinal fundus images using vessel segmentation

paper_content:

Fundus imaging is the most commonly used modality to collect information about the human eye background. Objective and quantitative assessment of quality for the acquired images is essential for manual, computer-aided and fully automatic diagnosis. In this paper, we present a no-reference quality metric to quantify image noise and blur and its application to fundus image quality assessment. The proposed metric takes the vessel tree visible on the retina as guidance to determine an image quality score. In our experiments, the performance of this approach is demonstrated by correlation analysis with the established full-reference metrics peak-signal-to-noise ratio (PSNR) and structural similarity (SSIM). We found a Spearman rank correlation for PSNR and SSIM of 0.89 and 0.91. For real data, our metric correlates reasonable to a human observer, indicating high agreement to human visual perception.
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paper_title: Automatic fundus image field detection and quality assessment

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Fundus images are an important diagnostic tool for many retinal diseases. Sometimes the images captured are of low quality and cannot be used for diagnosis requiring repeat image acquisition. So, it is efficient to have an automatic system to assess the quality of the fundus image during the time of image capture. We have developed an automatic approach to assess the quality of the acquired fundus image based upon the inherent symmetry of retinal vessels. We approach the problem of quality assessment in two ways-individual quality assessment of a single fundus image and comprehensive quality assessment of a set of three fundus images of different fields of an eye. Our method also detects the field and side of the fundus image using the position of optic disc and the intensity information in two local windows.
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paper_title: Identification of suitable fundus images using automated quality assessment methods

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Retinal image quality assessment (IQA) is a crucial process for automated retinal image analysis systems to obtain an accurate and successful diagnosis of retinal diseases. Consequently, the first step in a good retinal image analysis system is measuring the quality of the input image. We present an approach for finding medically suitable retinal images for retinal diagnosis. We used a three-class grading system that consists of good, bad, and outlier classes. We created a retinal image quality dataset with a total of 216 consecutive images called the Diabetic Retinopathy Image Database. We identified the suitable images within the good images for automatic retinal image analysis systems using a novel method. Subsequently, we evaluated our retinal image suitability approach using the Digital Retinal Images for Vessel Extraction and Standard Diabetic Retinopathy Database Calibration level 1 public datasets. The results were measured through the F1 metric, which is a harmonic mean of precision and recall metrics. The highest F1 scores of the IQA tests were 99.60%, 96.50%, and 85.00% for good, bad, and outlier classes, respectively. Additionally, the accuracy of our suitable image detection approach was 98.08%. Our approach can be integrated into any automatic retinal analysis system with sufficient performance scores.
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paper_title: Colour Retinal Image Enhancement Based on Domain Knowledge

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Retinal images are widely used to manually or automatically detect and diagnose many diseases. Due to the complex imaging setup, there is a large luminosity and contrast variability within and across images. Here, we use the knowledge of the imaging geometry and propose an enhancement method for colour retinal images, with a focus on contrast improvement with no introduction of artifacts. The method uses non-uniform sampling to estimate the degradation and derive a correction factor from a single plane. We also propose a scheme for applying the derived correction factor to enhance all the colour planes of a given image. The proposed enhancement method has been tested on a publicly available dataset. Results show marked improvement over existing methods.
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paper_title: Color Retinal Image Enhancement Based on Luminosity and Contrast Adjustment

paper_content:

Objective: Many common eye diseases and cardiovascular diseases can be diagnosed through retinal imaging. However, due to uneven illumination, image blurring, and low contrast, retinal images with poor quality are not useful for diagnosis, especially in automated image analyzing systems. Here, we propose a new image enhancement method to improve color retinal image luminosity and contrast. Methods: A luminance gain matrix, which is obtained by gamma correction of the value channel in the HSV (hue, saturation, and value) color space, is used to enhance the R, G, and B (red, green and blue) channels, respectively. Contrast is then enhanced in the luminosity channel of L*a*b* color space by CLAHE (contrast-limited adaptive histogram equalization). Image enhancement by the proposed method is compared to other methods by evaluating quality scores of the enhanced images. Results: The performance of the method is mainly validated on a dataset of 961 poor-quality retinal images. Quality assessment (range 0–1) of image enhancement of this poor dataset indicated that our method improved color retinal image quality from an average of 0.0404 (standard deviation 0.0291) up to an average of 0.4565 (standard deviation 0.1000). Conclusion: The proposed method is shown to achieve superior image enhancement compared to contrast enhancement in other color spaces or by other related methods, while simultaneously preserving image naturalness. Significance: This method of color retinal image enhancement may be employed to assist ophthalmologists in more efficient screening of retinal diseases and in development of improved automated image analysis for clinical diagnosis.
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paper_title: Enhancement of retinal fundus Image to highlight the features for detection of abnormal eyes

paper_content:

Medical image analysis to aid in clinical diagnosis is one of the research areas currently drawing intense interests of scientists and physicians. The retinal fundus photograph are widely used in the diagnosis and treatment of various eye diseases such as Diabetic Retinopathy, glaucoma etc. Diabetic Retinopathy is the leading cause of blindness in the working age population. If the disease is detected and treated early, many of the visual losses can be prevented. This paper describes the methods to detect main features of fundus images such as optic disk, fovea, and exudates and blood vessels. To determine the optic Disk and its centre we find the brightest part of the fundus and apply Hough transform. The candidate region of fovea is defined an area circle. The detection of fovea is done by using its spatial relationship with optic disk. Exudates are found using their high grey level variation and their contours are determined by means of morphological reconstruction techniques. The blood vessels are highlighted using bottom hat transform and morphological dilation after edge detection. All the enhanced features are then combined in the fundus image for the detection of abnormalities in the eye.
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paper_title: Contrast Enhancement of Retinal Vasculature in Digital Fundus Image

paper_content:

Analyzing retinal fundus image is important for early detection of diseases related to the eye. However, in fundus images the contrast between retinal blood vessels and the background is very low. Hence, analyzing or visualizing tiny blood vessels is difficult. Fluorescein angiogram overcomes this imaging problem but it is an invasive procedure that leads to other physiological problems. In this work, we enhance the low contrast of retinal vasculature in the ocular fundus image by determining the retinal pigments makeup, namely haemoglobin, melanin and macular pigment using independent component analysis. Independent component image due to haemoglobin pigment obtained exhibits higher contrast retinal vasculature. Even though the fundus fluorescein angiograph method produces higher contrast images, results show that this approach outperforms other noninvasive enhancement method, such as contrast limited adaptive histogram equalization and can be beneficial for vessel segmentation. Contrast enhancement factor of 3.2 for digital retinal images is achieved. This improvement in contrast reduces the need of applying contrasting agent on patients.
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paper_title: Improved automatic localization of optic disc in Retinal Fundus using image enhancement techniques and SVM

paper_content:

Fundus Image is the creation of a photograph of human eye interior surface, including retina, optic disc, macula, and posterior pole. Fundus photography is used by ophthalmologist's and trained medical professionals for monitoring progression of a disease, diagnosis of a disease or in screening programs and epidemiology. The optic disc is also the entry point for the major blood vessels that supply the retina. The detection and localization of optic disc from ophthalmic fundus images is very vital for the diagnosis of various diseases in ophthalmology. These diseases are varying from Glaucoma to Diabetic Retinopathy and many more. Image processing is widely used for the purpose of diagnosing and detection of different retinal diseases however, the results generated by these algorithms can only be tested by domain experts. This research aims at an image processing method for detecting optic disc. An algorithm is developed and generated results are verified by image processing algorithm for detection of optic disc using machine learning through Support Vector Machine (SVM). The algorithm is first trained on a set of images as marked by the domain expert (Ophthalmologist) and SVM training results are stored in a database. Image processing technique is used for the testing of unknown fundus image and its outcome is verified through SVM. Depending on SVM outcome, desired optic disc is marked on the image. The proposed framework uses image processing algorithm and then verify those results using SVM. The proposed method is tested on a known public repository MESSIDOR and STARE Fundus image database and applied accomplished results to 100 images.
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paper_title: Vessel enhancement of low quality fundus image using mathematical morphology and combination of Gabor and matched filter

paper_content:

Fundus images have been widely used in the diagnosis of retinopathy and cardiovascular diseases. However, because of the movement of patients' eyes and limitation of medical equipments, the quality of fundus images may be low sometimes. In this paper, we propose vessel enhancement method for a low-contrast and blurred image based on multi-scale morphological top-hat transformation, and the combination of Gabor and matched filter. The underlying rationale of this proposed method is to make use different kinds of information to improve blood vessels on retinal fundus images with poor quality. Experiments show that vessels enhanced by our proposed method are much clearer than the ones using original multi-scale morphological top-hat transformation, Gabor filter or matched filter. The results confirm that the quality of vessel enhancement can be improved by combining these methods.
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paper_title: Methods to enhance digital fundus image for diabetic retinopathy detection

paper_content:

Digital fundus images (DFIs) are crucial in detecting pathological phenomenon that would lead to various diseases. However, DFI has multiple contrast and illumination problems which makes enhancement a necessity. Consequently, DFI must be enhanced to allow for better visualization in order to facilitate ophthalmologists to carry out their diagnosis. In this work, an investigation of three enhancement methods namely, Histogram Equalization(HE), Contrast Limited Adaptive Histogram Equalization(CLAHE) and Mahalanobis Distance (MD), were conducted on the digital fundus images and the results are qualitatively presented using histogram representation and image product quality. The result shows that the MD is the best algorithm for the application of blood vessels image enhancement.
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paper_title: Contrast enhancement of fundus images using regional histograms for medical diagnosis

paper_content:

This paper proposes an algorithm of the contrast enhancement of the retinal fundus images using regional histograms. The proposed method is extracting the background of the retinal area firstly, then dividing it into several regions according to their illuminations. Lastly, we adjust the illuminations in each region and enhance the image contrast. We show the effectiveness in extracting blood vessels.
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paper_title: Pseudo no reference image quality metric using perceptual data hiding

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Regarding the important constraints due to subjective quality assessment, objective image quality assessment has recently been extensively studied. Such metrics are usually of three kinds, they might be Full Reference (FR), Reduced Reference (RR) or No Reference (NR) metrics. We focus here on a new technique, which recently appeared in quality assessment context: data-hiding-based image quality metric. Regarding the amount of data to be transmitted for quality assessment purpose, watermarking based techniques are considered as pseudo noreference metric: A little overhead due to the embedded watermark is added to the image. Unlike most existing techniques, the proposed embedding method exploits an advanced perceptual model in order to optimize both the data embedding and extraction. A perceptually weighted watermark is embedded into the host image, and an evaluation of this watermark allows to assess the host image's quality. In such context, the watermark robustness is crucial; it must be suffciently robust to be detected after very strong distortions, but it must also be suffciently fragile to be degraded along with the host image. In other words, the watermark distortion must be proportional to the image's distortion. Our work is compared to existing standard RR and NR metrics in terms of both the correlation with subjective assessment and of data overhead induced by the mark.
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paper_title: Reduced-reference metric design for objective perceptual quality assessment in wireless imaging

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The rapid growth of third and development of future generation mobile systems has led to an increase in the demand for image and video services. However, the hostile nature of the wireless channel makes the deployment of such services much more challenging, as in the case of a wireline system. In this context, the importance of taking care of user satisfaction with service provisioning as a whole has been recognized. The related user-oriented quality concepts cover end-to-end quality of service and subjective factors such as experiences with the service. To monitor quality and adapt system resources, performance indicators that represent service integrity have to be selected and related to objective measures that correlate well with the quality as perceived by humans. Such objective perceptual quality metrics can then be utilized to optimize quality perception associated with applications in technical systems. In this paper, we focus on the design of reduced-reference objective perceptual image quality metrics for use in wireless imaging. Specifically, the normalized hybrid image quality metric (NHIQM) and a perceptual relevance weighted L"p-norm are designed. The main idea behind both feature-based metrics relates to the fact that the human visual system (HVS) is trained to extract structural information from the viewing area. Accordingly, NHIQM and L"p-norm are designed to account for different structural artifacts that have been observed in our distortion model of a wireless link. The extent by which individual artifacts are present in a given image is obtained by measuring related image features. The overall quality measure is then computed as a weighting sum of the features with the respective perceptual relevance weight obtained from subjective experiments. The proposed metrics differ mainly in the pooling of the features and amount of reduced-reference produced. While NHIQM performs the pooling at the transmitter of the system to produce a single value as reduced-reference, the L"p-norm requires all involved feature values from the transmitted and received image to perform the pooling on the feature differences at the receiver. In addition, non-linear mapping functions are developed that relate the metric values to predicted mean opinion scores (MOS) and account for saturations in the HVS. The evaluation of prediction performance of NHIQM and the L"p-norm reveals their excellent correlation with human perception in terms of accuracy, monotonicity, and consistency. This holds not only for the prediction performance on images taken for the training of the metrics but also for the generalization to unknown images. In addition, it is shown that the NHIQM approach and the perceptual relevance weighted L"p-norm outperform other prominent objective quality metrics in prediction performance.
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Title: Fundus Image Quality Assessment: Survey, Challenges, and Future Scope

Section 1: Introduction
Description 1: Introduce the subject of Image Quality Assessment (IQA), different IQA methods, and their applications, focusing on the medical imaging field and the significance of fundus image quality.

Section 2: Need of new IQA methods for Fundus images
Description 2: Discuss the need for specific IQA methods tailored for fundus images, highlighting the differences between fundus images and natural images.

Section 3: Factors affecting the quality of fundus images
Description 3: Outline the factors that contribute to the degradation of the quality of retinal images, including common distortions and requirements for obtaining quality fundus images.

Section 4: Fundus image data-sets
Description 4: Provide details of publicly available retinal fundus image datasets used by researchers, including their characteristics and purposes.

Section 5: Survey of Fundus Image Quality Assessment Algorithms
Description 5: Present a detailed review of existing fundus IQA algorithms, categorized into similarity-based, segmentation-based, and machine learning-based methods.

Section 6: Challenges & Future scope
Description 6: Identify the unresolved challenges and limitations in the current state-of-the-art fundus IQA research and propose future research directions to address these issues.

Section 7: Conclusion
Description 7: Summarize the importance of fundus IQA, the current status of research, and the necessity for further developments, including the creation of comprehensive datasets and algorithms.