Image Compression: A Survey
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paper_title: Improving audio data quality and compression

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High data quality at low bit rate is an essential goal that people want to achieve. It is necessary to transfer data at low bit rate so that the bandwidth of the medium can be utilized efficiently. In most of the speech coding techniques the goal of low bit rate transfer is achieved but the data quality is affected badly. The proposed technique is an attempt to improve the data quality at low bit rate as well as fast transmission of data. The proposed technique protects the data quality by applying Linear Predictive Coding-10 and achieves the low bit rate by applying Quadrature Mirror Filter. A comprehensive analysis is on the basis of given parameters as size, compression time, Signal to Noise Ratio, power, energy, power in air, energy in air, mean, standard deviation and intensity.
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paper_title: Lossless Compression Method for Medical Image Sequences Using Super-Spatial Structure Prediction and Inter-frame Coding

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Space research organizations, hospitals and military air surveillance activities, among others, produce a huge amount of data in the form of images hence a large storage space is required to record this information. In hospitals, data produced during medical examination is in the form of a sequence of images and are very much correlated; because these images have great importance, some kind of lossless image compression technique is needed. Moreover, these images are often required to be transmitted over the network. Since the availability of storage and bandwidth is limited, a compression technique is required to reduce the number of bits to store these images and take less time to transmit them over the network. For this purpose, there are many state-of the-art lossless image compression algorithms like CALIC, LOCO-I, JPEG-LS, JPEG20000; Nevertheless, these compression algorithms take only a single file to compress and cannot exploit the correlation among the sequence frames of MRI or CE images. To exploit the correlation, a new algorithm is proposed in this paper. The primary goals of the proposed compression method are to minimize the memory resource during storage of compressed data as well as minimize the bandwidth requirement during transmission of compressed data. For achieving these goals, the proposed compression method combines the single image compression technique called super spatial structure prediction with inter-frame coding to acquire grater compression ratio. An efficient compression method requires elimination of redundancy of data during compression; therefore, for elimination of redundancy of data, initially, the super spatial structure prediction algorithm is applied with the fast block matching approach and later Huffman coding is applied for reducing the number of bits required for transmitting and storing single pixel value. Also, to speed up the block-matching process during motion estimation, the proposed method compares those blocks that have identical sum and leave the others; therefore, the time taken by the block-matching process is reduced by minimizing the unnecessary overhead during the blockmatching process. Thus, in the proposed fast lossless compression method for medical image sequences, the twostage redundant data elimination process ultimately reduces the memory resource required for storing and transmission. The method is tested on the sequences of MRI and CE images and produces an improved compression rate.
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paper_title: Brain Image Compression: A Brief Survey

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Brain image compression is known as a subfield of image compression. It allows the deep analysis and measurements of brain images in different modes. Brain images are compressed to analyze and diagnose in an effective manner while reducing the image storage space. This survey study describes the different existing techniques regarding brain image compression. The techniques come under different categories. The study also discusses these categories.
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paper_title: Region-based fractal image compression using heuristic search

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Presents work carried out on fractal (or attractor) image compression. The approach relies on the assumption that image redundancy can be efficiently exploited through self-transformability. The algorithms described utilize a novel region-based partition of the image that greatly increases the compression ratios achieved over traditional block-based partitionings. Due to the large search spaces involved, heuristic algorithms are used to construct these region-based transformations. Results for three different heuristic algorithms are given. The results show that the region-based system achieves almost double the compression ratio of the simple block-based system at a similar decompressed image quality. For the Lena image, compression ratios of 41:1 can be achieved at a PSNR of 26.56 dB. >
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paper_title: Study on Huber fractal image compression.

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In this paper, a new similarity measure for fractal image compression (FIC) is introduced. In the proposed Huber fractal image compression (HFIC), the linear Huber regression technique from robust statistics is embedded into the encoding procedure of the fractal image compression. When the original image is corrupted by noises, we argue that the fractal image compression scheme should be insensitive to those noises presented in the corrupted image. This leads to a new concept of robust fractal image compression. The proposed HFIC is one of our attempts toward the design of robust fractal image compression. The main disadvantage of HFIC is the high computational cost. To overcome this drawback, particle swarm optimization (PSO) technique is utilized to reduce the searching time. Simulation results show that the proposed HFIC is robust against outliers in the image. Also, the PSO method can effectively reduce the encoding time while retaining the quality of the retrieved image.
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paper_title: A progressive Lossless/Near-Lossless image compression algorithm

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A novel image compression technique is presented that incorporates progressive transmission and near-lossless compression in a single framework. Experimental performance of the proposed coder proves to be competitive with the state-of-the-art compression schemes.
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paper_title: Adaptive Approximate Nearest Neighbor Search for Fractal Image Compression

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Fractal image encoding is a computationally intensive method of compression due to its need to find the best match between image subblocks by repeatedly searching a large virtual codebook constructed from the image under compression. One of the most innovative and promising approaches to speed up the encoding is to convert the range-domain block matching problem to a nearest neighbor search problem. This paper presents an improved formulation of approximate nearest neighbor search based on orthogonal projection and pre-quantization of the fractal transform parameters. Furthermore, an optimal adaptive scheme is derived for the approximate search parameter to further enhance the performance of the new algorithm. Experimental results showed that our new technique is able to improve both the fidelity and compression ratio, while significantly reduce memory requirement and encoding time.
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paper_title: An efficient bath fractal transform-based image coding technique

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A computationally efficient image compression technique is presented, which is based on visual patterns as well as least squares fractal approximation by a self-affine system (SAS). Simulation studies demonstrate that the use of visual pattern-based technique can code the shade (flat) regions of an image more efficiently than fractal techniques. Furthermore, the proposed technique not only gives an encoding speed several times faster than other SAS-based fractal coding techniques but also achieves higher compression ratios and better fidelity.
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paper_title: Progressive decoding method for fractal image compression

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Fractal image compression is an efficient technique for compactly coding images, in which an image is encoded by a contractive transformation whose fixed point is close to the original image, and then is decoded by using an iteration procedure stemmed from the well known Banach fixed-point theorem. A new fixed-point iteration theorem with a control parameter is presented, which provides a novel iteration procedure that progressively approaches the fixed point of a contractive transformation and particularly reverts back to the conventional iteration procedure when the control parameter is set as one. Based on the new iteration procedure, a progressive decoding algorithm is proposed for fractal image compression, which does not need any specific fractal encoder and is useful for low bandwidth transmission. The experimental results demonstrate that the progressive fractal decoding is capable of controlling the decoding iteration procedure by varying the control parameter values and displaying progressively how the original image is obtained from a black image or another image at each step of the increasing iterations.
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paper_title: A fast encoding algorithm for fractal image compression using the DCT inner product

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In this paper, a fast encoding algorithm is developed for fractal image compression. At each search entry in the domain pool, the mean square error (MSE) calculations of the given range block and the eight dihedral symmetries of the domain block are obtained simultaneously in the frequency domain, in which the redundant computations are all eliminated in the new encoding algorithm. It is shown in software simulation that the encoding time is about six times faster than that of the baseline method with almost the same PSNR for the retrieved image. The fast algorithm is performed to deal with the eight dihedral symmetries at each search entry. Therefore, it can be applied to various enhanced algorithms which are equipped with quadtree, classification, and other mechanisms.
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paper_title: Progressive decoding method for fractal image compression

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Fractal image compression is an efficient technique for compactly coding images, in which an image is encoded by a contractive transformation whose fixed point is close to the original image, and then is decoded by using an iteration procedure stemmed from the well known Banach fixed-point theorem. A new fixed-point iteration theorem with a control parameter is presented, which provides a novel iteration procedure that progressively approaches the fixed point of a contractive transformation and particularly reverts back to the conventional iteration procedure when the control parameter is set as one. Based on the new iteration procedure, a progressive decoding algorithm is proposed for fractal image compression, which does not need any specific fractal encoder and is useful for low bandwidth transmission. The experimental results demonstrate that the progressive fractal decoding is capable of controlling the decoding iteration procedure by varying the control parameter values and displaying progressively how the original image is obtained from a black image or another image at each step of the increasing iterations.
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paper_title: Combining fractal image compression and vector quantization.

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In fractal image compression, the code is an efficient binary representation of a contractive mapping whose unique fixed point approximates the original image. The mapping is typically composed of affine transformations, each approximating a block of the image by another block (called domain block) selected from the same image. The search for a suitable domain block is time-consuming. Moreover, the rate distortion performance of most fractal image coders is not satisfactory. We show how a few fixed vectors designed from a set of training images by a clustering algorithm accelerates the search for the domain blocks and improves both the rate-distortion performance and the decoding speed of a pure fractal coder, when they are used as a supplementary vector quantization codebook. We implemented two quadtree-based schemes: a fast top-down heuristic technique and one optimized with a Lagrange multiplier method. For the 8 bits per pixel (bpp) luminance part of the 512 x 512 Lena image, our best scheme achieved a peak-signal-to-noise ratio of 32.50 dB at 0.25 bpp.
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paper_title: Fractal image compression with simple classification scheme in frequency domain

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In fractal image compression, the mean squared error (MSE) computations of the eight orientations of the domain blocks can be reduced into two groups of inner products in the frequency domain. A very simple classification scheme is presented for reducing the two groups of computations into one, which makes the encoder up to 4.7 times faster.
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paper_title: Differentiation applied to lossless compression of medical images

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Lossless compression of medical images using a proposed differentiation technique is explored. This scheme is based on computing weighted differences between neighboring pixel values. The performance of the proposed approach, for the lossless compression of magnetic resonance (MR) images and ultrasonic images, is evaluated and compared with the lossless linear predictor and the lossless Joint Photographic Experts Group (JPEG) standard. The residue sequence of these techniques is coded using arithmetic coding. The proposed scheme yields compression measures, in terms of bits per pixel, that are comparable with or lower than those obtained using the linear predictor and the lossless JPEG standard, respectively, with 8-b medical images. The advantages of the differentiation technique presented here over the linear predictor are: 1) the coefficients of the differentiator are known by the encoder and the decoder, which eliminates the need to compute or encode these coefficients, and 21 the computational complexity is greatly reduced. These advantages are particularly attractive in real time processing for compressing and decompressing medical images.
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paper_title: Medical image compression by discrete cosine transform spectral similarity strategy

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Due to bandwidth and storage limitations, medical images must be compressed before transmission and storage. However, the compression reduces the image fidelity, especially when the images are compressed at low bit rates. The reconstructed images suffer from blocking artifacts and the image quality is severely degraded under high compression ratios. In this paper, we present a strategy to increase the compression ratio with low computational burden and excellent decoded quality. We regard the discrete cosine transform as a bandpass filter to decompose a sub-block into equal-sized bands. After a band-gathering operation, a high similarity property among the bands is found. By utilizing the similarity property, the bit rate of compression can be greatly reduced. Meanwhile, the characteristics of the original image are not sacrificed. Thus, it can avoid the misdiagnosis of diseases. Simulations were carried out on different kinds of medical images to demonstrate that the proposed method achieves better performance when compared to other existing transform coding schemes, such as JPEG, in terms of bit rate and quality. For the case of angiogram images, the peak signal-to-noise-ratio gain is 13.5 dB at the same bit rate of 0.15 bits per pixel when compared to the JPEG compression. As for the other kinds of medical images, their benefits are not so obvious as for angiogram images; however, the gains for them are still 4-8 dB at high compression ratios. Two doctors were invited to verify the decoded image quality; the diagnoses of all the test images were correct when the compression ratios were below 20.
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paper_title: Warped discrete cosine transform and its application in image compression

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This paper introduces the concept of warped discrete cosine transform (WDCT) and an image compression algorithm based on the WDCT. The proposed WDCT is a cascade connection of a conventional DCT and all-pass filters whose parameters can be adjusted to provide frequency warping. Because only the first-order all-pass filters are considered, the WDCT can be implemented by a Laguerre network connected with the DCT. For the more efficient software implementation, we propose truncated and approximated FIR filter banks which can be used instead of the Laguerre network. As a result, the input-output relationship of the WDCT can be represented by a single matrix-vector multiplication, like the DCT. In the proposed image-compression scheme, the frequency response of the all-pass filter is controlled by a fixed set of parameters from which a specified warping parameter is used for a specified frequency range. Also, for each parameter, the corresponding WDCT matrices are computed a priori. For each image block, the best parameter is chosen from the set and the index is sent to the decoder as side information along with the result of corresponding WDCT matrix computation. At the decoder, an inverse WDCT is performed to reconstruct the image. The WDCT based compression outperforms the DCT based compression, for high bit rate applications and for images with high-frequency components. It results in 1.1-3.1-dB PSNR gain over conventional DCT at 1.5 bpp for natural images, and provides more gain for compound images with texts.
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paper_title: High-Quality DCT-Based Image Compression Using Partition Schemes

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This letter presents an advanced discrete cosine transform (DCT)-based image compression method that combines advantages of several approaches. First, an image is divided into blocks of different sizes by a rate-distortion-based modified horizontal-vertical partition scheme. Statistical redundancy of quantized DCT coefficients of each image block is reduced by a bit-plane dynamical arithmetical coding with a sophisticated context modeling. Finally, a post-filtering removes blocking artifacts in decompressed images. The proposed method provides significantly better compression than JPEG and other DCT-based techniques. Moreover, it outperforms JPEG2000 and other wavelet-based image coders
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paper_title: An adaptive 3-D discrete cosine transform coder for medical image compression

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In this communication, a new three-dimensional (3-D) discrete cosine transform (DCT) coder for medical images is presented. In the proposed method, a segmentation technique based on the local energy magnitude is used to segment subblocks of the image into different energy levels. Then, those subblocks with the same energy level are gathered to form a 3-D cuboid. Finally, 3-D DCT is employed to compress the 3-D cuboid individually. Simulation results show that the reconstructed images achieve a bit rate lower than 0.25 bit per pixel even when the compression ratios are higher than 35. As compared with the results by JPEG and other strategies, it is found that the proposed method achieves better qualities of decoded images than by JPEG and the other strategies.
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paper_title: Fractal Image Compression with Region-Based Functionality

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Region-based functionality offered by the MPEG-4 video compression standard is also appealing for still images, for example to permit object-based queries of a still-image database. A popular method for still-image compression is fractal coding. However, traditional fractal image coding uses rectangular range and domain blocks. Although new schemes have been proposed that merge small blocks into irregular shapes, the merging process does not, in general, produce semantically-meaningful regions. We propose a new approach to fractal image coding that permits region-based functionalities; images are coded region by region according to a previously-computed segmentation map. We use rectangular range and domain blocks, but divide boundary blocks into segments belonging to different regions. Since this prevents the use of standard dissimilarity measure, we propose a new measure adapted to segment shape. We propose two approaches: one in the spatial and one in the transform domain. While providing additional functionality, the proposed methods perform similarly to other tested methods in terms of PSNR but often result in images that are subjectively better. Due to the limited domain-block codebook size, the new methods are faster than other fractal coding methods tested. The results are very encouraging and show the potential of this approach for various internet and still-image database applications.
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paper_title: Wavelet tree classification and hybrid coding for image compression

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A hybrid coding system that uses a combination of set partition in hierarchical trees (SPIHT) and vector quantisation (VQ) for image compression is presented. Here, the wavelet coefficients of the input image are rearranged to form the wavelet trees that are composed of the corresponding wavelet coefficients from all the subbands of the same orientation. A simple tree classifier has been proposed to group wavelet trees into two classes based on the amplitude distribution. Each class of wavelet trees is encoded using an appropriate procedure, specifically either SPIHT or VQ. Experimental results show that advantages obtained by combining the superior coding performance of VQ and efficient cross-subband prediction of SPIHT are appreciable for the compression task, especially for natural images with large portions of textures. For example, the proposed hybrid coding outperforms SPIHT by 0.38 dB in PSNR at 0.5 bpp for the Bridge image, and by 0.74 dB at 0.5 bpp for the Mandrill image.
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paper_title: Progressive medical image transmission and compression

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Digital radiology places very high demands on the networking and digital storage infrastructure of hospitals. In addition to having quite stringent requirements on the quality of the images displayed to the radiologist, much of the technical challenge resides in the necessity of displaying desired images as rapidly as possible. We present an infrastructure for progressive transmission and compression of medical images, which can refine an initial image by increasing the detail information not only in scale-space, but also in coefficient precision. The approach is based on the embedded zerotree wavelet (EZW) algorithm. This algorithm offers a tremendous amount of flexibility in meeting the bandwidth and image quality constraints in a radiological imaging environment. We propose an application of the EZW algorithm in progressive medical image transmission in which it can specify and control both the resolution constraint and rate constraint. The presented method can provide a framework for lossy image compression, with performance far superior to those provided by the standard JPEG algorithm. Also due to the flexibility of the method we will show how any region of interest of an image can be sent progressively.
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paper_title: Reversible EZW-based image compression using best-transform selection and selective partial embedding

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A best-transform selection method and a selective partial embedding technique are proposed for use in a reversible embedded image compression system based on the embedded zerotree wavelet (EZW) coding scheme. Together, these approaches are shown to yield significantly better compression performance than a baseline system employing fully embedded EZW coding in conjunction with a single fixed transform, such as the S+P transform.
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paper_title: A new method of robust image compression based on the embedded zerotree wavelet algorithm

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We propose a wavelet-based image compression algorithm that achieves robustness to transmission errors by partitioning the transform coefficients into groups and independently processing each group using an embedded coder. Thus, a bit error in one group does not affect the others, allowing more uncorrupted information to reach the decoder.
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paper_title: A two-layered wavelet-based algorithm for efficient lossless and lossy image compression

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In this paper, we propose a wavelet-based image-coding scheme allowing lossless and lossy compression, simultaneously. Our two-layered approach utilizes the best of two worlds: it uses a highly performing wavelet-based or wavelet packet-based coding technique for lossy compression in the low bit range as a first stage. For the second (optional) stage, we extend the concept of reversible integer wavelet transforms to the more flexible class of adaptive reversible integer wavelet packet transforms which are based on the generation of a whole library of bases, from which the best representation for a given residue between the reconstructed lossy compressed image and the original image is chosen using a fast-search algorithm. We present experimental results demonstrating that our compression algorithm yields a rate-distortion performance similar or superior to the best currently published pure lossy still image-coding methods. At the same time, the lossless compression performance of our two-layered scheme is comparable to that of state-of-the-art pure lossless image-coding schemes. Compared to other combined lossy/lossless coding schemes such as the emerging JPEG-2000 still image-coding standard PSNR improvements up to 3 dB are achieved for a set of standard test images.
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paper_title: An Improved Image Compression Algorithm Using Binary Space Partition Scheme and Geometric Wavelets

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Geometric wavelet is a recent development in the field of multivariate nonlinear piecewise polynomials approximation. The present study improves the geometric wavelet (GW) image coding method by using the slope intercept representation of the straight line in the binary space partition scheme. The performance of the proposed algorithm is compared with the wavelet transform-based compression methods such as the embedded zerotree wavelet (EZW), the set partitioning in hierarchical trees (SPIHT) and the embedded block coding with optimized truncation (EBCOT), and other recently developed "sparse geometric representation" based compression algorithms. The proposed image compression algorithm outperforms the EZW, the Bandelets and the GW algorithm. The presented algorithm reports a gain of 0.22 dB over the GW method at the compression ratio of 64 for the Cameraman test image.
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paper_title: Performance Analysis of Image Compression Using Wavelets

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The aim of this paper is to examine a set of wavelet functions (wavelets) for implementation in a still image compression system and to highlight the benefit of this transform relating to today's methods. The paper discusses important features of wavelet transform in compression of still images, including the extent to which the quality of image is degraded by the process of wavelet compression and decompression. Image quality is measured objectively, using peak signal-to-noise ratio or picture quality scale, and subjectively, using perceived image quality. The effects of different wavelet functions, image contents and compression ratios are assessed. A comparison with a discrete-cosine-transform-based compression system is given. Our results provide a good reference for application developers to choose a good wavelet compression system for their application.
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paper_title: Wavelet-based space-frequency compression of ultrasound images

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This paper describes the compression of grayscale medical ultrasound images using a recent compression technique, i.e., space-frequency segmentation (SITS). This method finds the rate-distortion optimal representation of an image from a large set of possible space-frequency partitions and quantizer combinations and is especially effective when the images to code are statistically inhomogeneous, which is the case for medical ultrasound images. We implemented a compression application based on this method and tested the algorithm on representative ultrasound images. The result is an effective technique that performs better than a leading wavelet-transform coding algorithm, i.e., set partitioning in hierarchical trees (SPIHT), using standard objective distortion measures. To determine the subjective qualitative performance, an expert viewer study was run by presenting ultrasound radiologists with images compressed using both SFS and SPIHT. The results confirmed the objective performance rankings. Finally, the performance sensitivity of the space-frequency codec is shown with respect to several parameters, and the characteristic space-frequency partitions found for ultrasound images are discussed.
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paper_title: Cascaded differential and wavelet compression of chromosome images

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This paper proposes a new method for chromosome image compression based on an important characteristic of these images: the regions of interest (ROIs) to cytogeneticists for evaluation and diagnosis are well determined and segmented. Such information is utilized to advantage in our compression algorithm, which combines lossless compression of chromosome ROIs with lossy-to-lossless coding of the remaining image parts. This is accomplished by first performing a differential operation on chromosome ROIs for decorrelation, followed by critically sampled integer wavelet transforms on these regions and the remaining image parts. The well-known set partitioning in hierarchical trees (SPIHT) (Said and Perlman, 1996) algorithm is modified to generate separate embedded bit streams for both chromosome ROIs and the rest of the image that allow continuous lossy-to-lossless compression of both (although lossless compression of the former is commonly used in practice). Experiments on two sets of sample chromosome spread and karyotype images indicate that the proposed approach significantly outperforms current compression techniques used in commercial karyotyping systems and JPEG-2000 compression, which does not provide the desirable support for lossless compression of arbitrary ROIs.
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paper_title: Image compression based on energy clustering and zero-quadtree representation

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An efficient image compression algorithm based on energy clustering and zero-quadtree representation (ECZQR) in the wavelet transform domain is proposed. In embedded coding, zeros within each subband are encoded in the framework of quadtree representation instead of zerotree representation. To use large rectangular blocks to represent zeros, it first uses morphological dilation to extract the arbitrarily shaped clusters of significant coefficients within each subband. The proposed encoding method results in less distortion in the decoded image than the line-by-line encoding method. Experimental results show that the algorithm is among the most efficient wavelet image compression algorithms.
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paper_title: The predictive embedded zerotree wavelet (PEZW) coder: low complexity image coding with versatile functionality

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We introduce the predictive embedded zerotree wavelet (PEZW) codec, an image coder that achieves good coding efficiency and versatile functionality with limited complexity requirement. Our complexity analysis showed that the memory requirement of this coder is less than 15 k bytes regardless of image sizes. Our simulation results also showed that the coding efficiency of this low complexity coder is competitive with the state of the art of wavelet coders that use whole image buffers. The PEZW coder described has been adopted in MPEG4 as its still texture coding tool and is currently a proposal to the evolving JPEG2000 standard.
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paper_title: A Near Lossless Wavelet-Based Compression Scheme for Satellite Images

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In this paper, a near lossless image compression algorithm is presented for high quality satellite image compression. The proposed algorithm makes use of the recommendation for image data compression from the Consultative Committee for Space Data Systems (CCSDS) and specific residue image bit-plane compensation. Comparing with the recommendation for satellite image compression from CCSDS, the proposed algorithm can reconstruct near lossless images with less bit rate than the recommendation of CCSDS does. Benefited from run-length coding and specific residue image bit-plane compensation, the proposed algorithm can obtain higher quality satellite image at similar bit rate or lower bit rate at the similar image quality. These results are valuable for reducing transmission time of high quality satellite image data. This work can be further improved by combining other binary compression techniques and the extension of this work may offer a VLSI or a DSP implementation of the proposed algorithm. Satellite image transmission and storage system can benefit by the proposed algorithm.
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paper_title: Embedded image compression based on wavelet pixel classification and sorting

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A new embedded image compression algorithm is proposed, based on progressive Pixel Classification And Sorting (PCAS) in wavelet domain. To exploit the intraband and interband correlation in wavelet domain, EZW [1] and SPIHT [2] implicitly classify wavelet pixels as zerotree pixels or not, while MRWD[3], SLCCA[4], and EBCOT[5] implicitly classify wavelet pixels as neighbors of significant pixels or not. In this paper, the wavelet pixels to be encoded are explicitly and finely classified based on their predicted probabilities, which is more sophisticated and effective. Furthermore, wavelet pixel sorting is introduced to help improve rate-distortion performance within each bit-plane coding. The technique of pixel classification and sorting is simple, yet effective to produce the image code with excellent compression performance. In addition, our algorithm provides both SNR and resolution scalability.
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paper_title: Entropy-constrained scalar quantization and minimum entropy with error bound by discrete wavelet transforms in image compression

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The global maximum of an entropy function with different decision levels for a three-level scalar quantizer performed after a discrete wavelet transform was derived. Herein, we considered the case of entropy-constrained scalar quantization capable of avoiding many compression ratio reductions as the mean squared error was minimized. We also dealt with the problem of minimum entropy with an error bound, which was referred to as the rate distortion function. For generalized Gaussian distributed input signals, the Shannon bound would decrease monotonically when the parameter of distribution /spl gamma/ was to leave from 2. That is Gaussian distributions would contain the highest Shannon bound among the generalized Gaussian distributions. Additionally, we proposed two numerical approaches of the secant and false position methods implemented in real cases to solve the problems of entropy-constrained scalar quantization and minimum entropy with an error bound. The convergence condition of the secant method was also addressed.
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paper_title: Convergent algorithms for successive approximation vector quantisation with applications to wavelet image compression

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Embedded wavelet coders have become very popular in image compression applications, owing to their simplicity and high coding efficiency. Most of them incorporate some form of successive approximation scalar quantisation. Recently developed algorithms for successive approximation vector quantisation have been shown to be capable of outperforming successive approximation scalar quantisation ones. In the paper, some algorithms for successive approximation vector quantisation are analysed. Results that were previously known only on an experimental basis are derived analytically. An improved algorithm is also developed and is proved to be convergent. These algorithms are applied to the coding of wavelet coefficients of images. Experimental results show that the improved algorithm is more stable in a rate/spl times/distortion sense, while maintaining coding performances compatible with the state-of-the-art.
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paper_title: Image compression through embedded multiwavelet transform coding.

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In this paper, multiwavelets are considered in the context of image compression and two orthonormal multiwavelet bases are experimented, each used in connection with its proper prefilter. For evaluating the effectiveness of multiwavelet transform for coding images at low bit-rates, an efficient embedded coding of multiwavelet coefficients has been realized. The performance of this multiwavelet-based coder is compared with the results obtained for scalar wavelets.
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paper_title: Wavelet-based Color Image Compression: Exploiting the Contrast Sensitivity Function

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The visual efficiency of an image compression technique depends directly on the amount of visually significant information it retains. By "visually significant" we mean information to which a human observer is most sensitive. The overall sensitivity depends on aspects such as contrast, color, spatial frequency, and so forth. One important aspect is the inverse relationship between contrast sensitivity and spatial frequency. This is described by the contrast sensitivity function (CSF). In compression algorithms the CSF can be exploited to regulate the quantization step-size to minimize the visibility of compression artifacts. Existing CSF implementations for wavelet-based image compression use the same quantization step-size for a large range of spatial frequencies. This is a coarse approximation of the CSF. This paper presents two new techniques that implement the CSF at significantly higher precision, adapting even to local variations of the spatial frequencies within a decomposition subband. The approaches can be used for luminance as well as color images. For color perception three different CSFs describe the sensitivity. The implementation technique is the same for each color band. Implemented into the JPEG2000 compression standard, the new techniques are compared to conventional CSF-schemes. The proposed techniques turn out to be visually more efficient than previously published methods. However, the emphasis of this paper is on how the CSF can be implemented in a precise and locally adaptive way, and not on the superior performance of these techniques.
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paper_title: Line Based, Reduced Memory, Wavelet Image Compression

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This paper addresses the problem of low memory wavelet image compression. While wavelet or subband coding of images has been shown to be superior to more traditional transform coding techniques, little attention has been paid until recently to the important issue of whether both the wavelet transforms and the subsequent coding can be implemented in low memory without significant loss in performance. We present a complete system to perform low memory wavelet image coding. Our approach is "line-based" in that the images are read line by line and only the minimum required number of lines is kept in memory. There are two main contributions of our work. First, we introduce a line-based approach for the implementation of the wavelet transform, which yields the same results as a "normal" implementation, but where, unlike prior work, we address memory issues arising from the need to synchronize encoder and decoder. Second, we propose a novel context-based encoder which requires no global information and stores only a local set of wavelet coefficients. This low memory coder achieves performance comparable to state of the art coders at a fraction of their memory utilization.
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paper_title: Dynamic contrast-based quantization for lossy wavelet image compression

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This paper presents a contrast-based quantization strategy for use in lossy wavelet image compression that attempts to preserve visual quality at any bit rate. Based on the results of recent psychophysical experiments using near-threshold and suprathreshold wavelet subband quantization distortions presented against natural-image backgrounds, subbands are quantized such that the distortions in the reconstructed image exhibit root-mean-squared contrasts selected based on image, subband, and display characteristics and on a measure of total visual distortion so as to preserve the visual system's ability to integrate edge structure across scale space. Within a single, unified framework, the proposed contrast-based strategy yields images which are competitive in visual quality with results from current visually lossless approaches at high bit rates and which demonstrate improved visual quality over current visually lossy approaches at low bit rates. This strategy operates in the context of both nonembedded and embedded quantization, the latter of which yields a highly scalable codestream which attempts to maintain visual quality at all bit rates; a specific application of the proposed algorithm to JPEG-2000 is presented.
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paper_title: Fast spatial combinative lifting algorithm of wavelet transform using the 9/7 filter for image block compression

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A new fast spatial combinative lifting algorithm (SCLA) of the wavelet transform using the 9/7 filter for image block compression is proposed. In comparison with its lifting-based implementation, the number of multiplications is reduced by a ratio of 5/12 and the speed of implementation of the wavelet transform is increased.
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paper_title: Fast adaptive wavelet packet image compression

paper_content:

Wavelets are ill-suited to represent oscillatory patterns: rapid variations of intensity can only be described by the small scale wavelet coefficients, which are often quantized to zero, even at high bit rates. Our goal is to provide a fast numerical implementation of the best wavelet packet algorithm in order to demonstrate that an advantage can be gained by constructing a basis adapted to a target image. Emphasis is placed on developing algorithms that are computationally efficient. We developed a new fast two-dimensional (2-D) convolution decimation algorithm with factorized nonseparable 2-D filters. The algorithm is four times faster than a standard convolution-decimation. An extensive evaluation of the algorithm was performed on a large class of textured images. Because of its ability to reproduce textures so well, the wavelet packet coder significantly out performs one of the best wavelet coder on images such as Barbara and fingerprints, both visually and in term of PSNR.
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paper_title: Error-tolerant SPIHT image compression

paper_content:

A new, error-tolerant, still image compression algorithm is described. An error-resilient adaptation of an existing zerotree wavelet coder is used to compress the image. Then, a variable-rate, concatenated error correction code is applied to protect the data further. Variable-rate protection exploits the progressive coding ability of the compressor, encouraging uncorrected errors to appear in the least significant portions of the bit-stream during decoding, which lessens their effect on the final image. The error-resilient organisation of the information inhibits the propagation of uncorrected errors, ensuring graceful degradation of the image over a wide range of bit error rates. Results for the popular 512×512 ‘Lena’ image are presented, showing notable improvements over previously published algorithms.
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paper_title: SAR Image Data Compression Using a Tree-Structured Wavelet Transform

paper_content:

SAR image compression is very important in reducing the costs of data storage and transmission in relatively slow channels. The authors propose a compression scheme driven by texture analysis, homogeneity mapping and speckle noise reduction within the wavelet framework. The image compressibility and interpretability are improved by incorporating speckle reduction into the compression scheme. The authors begin with the classical set partitioning in hierarchical trees (SPIHT) wavelet compression scheme, and modify it to control the amount of speckle reduction, applying different encoding schemes to homogeneous and nonhomogeneous areas of the scene. The results compare favorably with the conventional SPIHT wavelet and the JPEG compression methods.
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paper_title: An efficient spatial prediction-based image compression scheme

paper_content:

We have designed a spatial prediction-based image-compression scheme. The proposed scheme consists of two phases: the prediction phase and the quantization phase. In the prediction phase, a hierarchical structure among pixels in the image is built. Following the constructed hierarchical structure, the neighboring pixels are utilized to predict every central pixel. The prediction scheme generates an image map which indicates the prediction errors. The structure of the resulting image map is very similar to the result of a discrete wavelet transform. Thus, most quantization methods of wavelet or subband image-compression algorithms can be followed in our scheme directly to yield good compression performance. In the quantization phase, we design a multilevel threshold scheme to further enhance the result of SPIHT by taking the significance of the pixel values and the hierarchical levels into account. Furthermore, the proposed scheme can be realized by only a few integer additions and bit shifts. Simulation results indicate that the visual quality of the designed efficient spatial prediction-based image compression scheme is competitive with JPEG. All the above features make the designed image-compression scheme beneficial to the applications of real-time and wireless transmission in low-computational power environments.
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paper_title: 2D-pattern matching image and video compression: theory, algorithms, and experiments

paper_content:

In this paper, we propose a lossy data compression framework based on an approximate two-dimensional (2D) pattern matching (2D-PMC) extension of the Lempel-Ziv (1977, 1978) lossless scheme. This framework forms the basis upon which higher level schemes relying on differential coding, frequency domain techniques, prediction, and other methods can be built. We apply our pattern matching framework to image and video compression and report on theoretical and experimental results. Theoretically, we show that the fixed database model used for video compression leads to suboptimal but computationally efficient performance. The compression ratio of this model is shown to tend to the generalized entropy. For image compression, we use a growing database model for which we provide an approximate analysis. The implementation of 2D-PMC is a challenging problem from the algorithmic point of view. We use a range of techniques and data structures such as k-d trees, generalized run length coding, adaptive arithmetic coding, and variable and adaptive maximum distortion level to achieve good compression ratios at high compression speeds. We demonstrate bit rates in the range of 0.25-0.5 bpp for high-quality images and data rates in the range of 0.15-0.5 Mbps for a baseline video compression scheme that does not use any prediction or interpolation. We also demonstrate that this asymmetric compression scheme is capable of extremely fast decompression making it particularly suitable for networked multimedia applications.
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paper_title: Efficient compression algorithm for hyperspectral images based on correlation coefficients adaptive 3D zerotree coding

paper_content:

The authors propose an efficient compression algorithm for hyperspectral images, which is based on the correlation coefficients adaptive asymmetric tree three-dimensional (3D) set partitioning in hierarchical trees (AT-3DSPIHT) coding of the asymmetric 3D wavelet transform (A3D-DWT) coefficients. According to the characteristics of the correlation coefficients between adjacent spectral bands, a binary tree spectral band grouping algorithm is carried out to divide the adjacent spectral bands into different mode groups. Along with this, an appropriate A3D-DWT with the corresponding decomposition levels and a proper AT-3D zerotree are determined adaptively. Several airborne visible/infrared imaging spectrometer images are used to evaluate the proposed algorithm. Compared with the state-of-the-art 3D-DWT based algorithms, the proposed adaptive AT-3DSPIHT achieves the best compression performance at lower bit rates. Moreover, at the low correlated adjacent bands, the proposed algorithm also outperforms the 2DSPIHT algorithm.
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paper_title: Image and video compression algorithms based on recovery techniques using mean field annealing

paper_content:

Image and video coding algorithms have found a number of applications ranging from video telephony on the public switched telephone networks (PSTN) to HDTV. However, as the bit rate is lowered, most of the existing techniques, as well as current standards, such as JPEG, H. 261, and MPEG-1 produce highly visible degradations in the reconstructed images primarily due to the information loss caused by the quantization process. In this paper, we propose an iterative technique to reduce the unwanted degradations, such as blocking and mosquito artifacts while keeping the necessary detail present in the original image. The proposed technique makes use of a priori information about the original image through a nonstationary Gauss-Markov model. Utilizing this model, a maximum a posteriori (MAP) estimate is obtained iteratively using mean field annealing. The fidelity to the data is preserved by projecting the image onto a constraint set defined by the quantizer at each iteration. The proposed solution represents an implementation of a paradigm we advocate, according to which the decoder is not simply undoing the operations performed by the encoder, but instead it solves an estimation problem based on the available bitstream and any prior knowledge about the source image. The performance of the proposed algorithm was tested on a JPEG, as well as on an H.261-type video codec. It is shown to be effective in removing the coding artifacts present in low bit rate compression. >
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paper_title: Hybrid compression of video with graphics in DTV communication systems

paper_content:

Advanced broadcast manipulation of TV sequences and enhanced user interfaces for TV systems have resulted in an increased amount of pre- and post-editing of video sequences, where graphical information is inserted. However, in the current broadcasting chain, there are no provisions for enabling an efficient transmission/storage of these mixed video and graphics signals and, at this emerging stage of DTV systems, introducing new standards is not desired. Nevertheless, in the professional video communication chain between content provider and broadcaster and locally, in the DTV receiver, proprietary video-graphics compression schemes can be used to enable more efficient transmission/storage of mixed video and graphics signals. For example, in the DTV receiver case this will lead to a significant memory-cost reduction. To preserve a high overall image quality, the video and graphics data require independent coding systems, matched with their specific visual and statistical properties. We introduce various efficient algorithms that support both the lossless (contour, runlength and arithmetic coding) and the lossy (block predictive coding) compression of graphics data. If the graphics data are a-priori mixed with video and the graphics position is unknown at compression time, an accurate detection mechanism is applied to distinguish the two signals, such that independent coding algorithms can be employed for each data-type. In the DTV memory-reduction scenario, an overall bit-rate control completes the system, ensuring a fixed compression factor of 2-3 per frame without sacrificing the quality of the graphics.
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paper_title: Hybrid image compression model based on subband coding and edge-preserving regularisation

paper_content:

An edge-preserving image compression model is presented, based on subband coding and iterative constrained least square regularisation. The idea is to incorporate the technique of image restoration into the current lossy image compression schemes. The model utilises the edge information extracted from the source image as a priori knowledge for the subsequent reconstruction. Generally, the extracted edge information has a limited range of magnitudes and it can be lossily conveyed. Subband coding, one of the outstanding lossy image compression schemes, is incorporated to compress the source image. Vector quantisation, a block-based lossy compression technique, is employed to compromise the bit rate incurred by the additional edge information and the target bit rate. Experiments show that the approach could significantly improve both the objective and subjective quality of the reconstructed image by preserving more edge details. Specifically, the model incorporated with SPIHT (set partitioning in hierarchical trees) outperformed the original SPIHT with the "Baboon" continuous-tone test image. In general, the model may be applied to any lossy image compression systems.
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paper_title: Optimal block boundary pre/postfiltering for wavelet-based image and video compression

paper_content:

This paper presents a pre/postfiltering framework to reduce the reconstruction errors near block boundaries in wavelet-based image and video compression. Two algorithms are developed to obtain the optimal filter, based on boundary filter bank and polyphase structure, respectively. A low-complexity structure is employed to approximate the optimal solution. Performances of the proposed method in the removal of JPEG 2000 tiling artifact and the jittering artifact of three-dimensional wavelet video coding are reported. Comparisons with other methods demonstrate the advantages of our pre/postfiltering framework.
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paper_title: Lossless medical-image compression using multiple array technique

paper_content:

The multiple array technique proposed in this paper is a lossless compression technique that involves no transformation, and achieves a very good compression ratio. This technique is based on forming three arrays to indicate and code the changes in the adjacent pixels. The arrays thus formed can be further compressed using Huffman coding. The computational complexity is greatly reduced thus producing faster compressions and decompressions. Due to the combination of simplicity and compression potential of this technique, the algorithm "enjoys the best of both worlds". This technique attains compression ratios similar to or superior than the lossless image compression standards presently available. Due to the high compression ratio, the average bits required to represent a pixel is greatly reduced. Thus, this algorithm is optimally suited for telemedicine having fast transmission rates.
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paper_title: A new compression scheme for color-quantized images

paper_content:

An efficient compression scheme for color-quantized images based on progressive coding of color information has been developed. Instead of sorting color indexes into a linear list struc- ture, a binary-tree structure of color indexes is proposed. With this structure, the new algorithm can progressively recover an image from two colors to all of the colors contained in the original image, i.e., a lossless recovery is achieved. Experimental results showed that it can efficiently compress images in both lossy and lossless cases. Typically for color-quantized Lena image with 256 colors, the algorithm achieved 0.5 bpp below state-of-the-art lossless com- pression methods while preserving the efficient lossy compression. Such a compression scheme is very attractive to many applications that require the ability of fast browsing or progressive transmis- sion, and if necessary, to exactly recover the original image. Index Terms—Color-quantized images, data compression, index sorting, progressive refinement.
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paper_title: Lossless image compression with a codebook of block scans

paper_content:

When applying predictive compression on image data there is an implicit assumption that the image is scanned in a particular order. Clearly, depending on the image, a different scanning order may give better compression. In earlier work, we had defined the notion of a prediction tree (or scan) which defines a scanning order for an image. An image can be decorrelated by taking differences among adjacent pixels along any traversal of a scan. Given an image, an optimal scan that minimizes the absolute sum of the differences encountered can be computed efficiently. However, the number of bits required to encode an optimal scan turns out to be prohibitive for most applications. In this paper we present a prediction scheme that partitions an image into blocks and for each block selects a scan from a codebook of scans such that the resulting prediction error is minimized. Techniques based on clustering are developed for the design of a codebook of scans. Design of both semiadaptive and adaptive codebooks is considered. We also combine the new prediction scheme with an effective error modeling scheme. Implementation results are then given, which compare very favorably with the JPEG lossless compression standard. >
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paper_title: Lossless image compression by using gradient adjusted prediction and Burrows-Wheeler transformation

paper_content:

In general text compression techniques cannot be used directly in image compression because the model of text and image are different. Previously, a new class of text compression, namely, the block-sorting algorithm which involves Burrows and Wheeler (1994) transformation (BWT) gave excellent results in text compression. However, if we apply it directly in image compression, the result is poor. Surprisingly, good results can be obtained if we employ a prediction model such as the one defined in the JPEG standard before the BWT algorithm. Thus, the predictive model plays a critical role in the compression process. To further improve the compression efficiency, we use the gradient adjusted prediction (GAP). Experimental results show that the proposed method is better than lossless JPEG and some LZ-based compression methods.
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paper_title: The LOCO-I Lossless Image Compression Algorithm : Principles and Standardization into JPEG-LS

paper_content:

LOCO-I (LOw COmplexity LOssless COmpression for Images) is the algorithm at the core of the new ISO/ITU standard for lossless and near-lossless compression of continuous-tone images, JPEG-LS. It is conceived as a "low complexity projection" of the universal context modeling paradigm, matching its modeling unit to a simple coding unit. By combining simplicity with the compression potential of context models, the algorithm "enjoys the best of both worlds." It is based on a simple fixed context model, which approaches the capability of the more complex universal techniques for capturing high-order dependencies. The model is tuned for efficient performance in conjunction with an extended family of Golomb (1966) type codes, which are adaptively chosen, and an embedded alphabet extension for coding of low-entropy image regions. LOCO-I attains compression ratios similar or superior to those obtained with state-of-the-art schemes based on arithmetic coding. Moreover, it is within a few percentage points of the best available compression ratios, at a much lower complexity level. We discuss the principles underlying the design of LOCO-I, and its standardization into JPEC-LS.
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paper_title: Texture- and Multiple-Template-Based Algorithm for Lossless Compression of Error-Diffused Images

paper_content:

Recently, several efficient context-based arithmetic coding algorithms have been developed successfully for lossless compression of error-diffused images. In this paper, we first present a novel block- and texture-based approach to train the multiple-template according to the most representative texture features. Based on the trained multiple template, we next present an efficient texture- and multiple-template-based (TM-based) algorithm for lossless compression of error-diffused images. In our proposed TM-based algorithm, the input image is divided into many blocks and for each block, the best template is adaptively selected from the multiple-template based on the texture feature of that block. Under 20 testing error-diffused images and the personal computer with Intel Celeron 2.8-GHz CPU, experimental results demonstrate that with a little encoding time degradation, 0.365 s (0.901 s) on average, the compression improvement ratio of our proposed TM-based algorithm over the joint bilevel image group (JBIG) standard [over the previous block arithmetic coding for image compression (BACIC) algorithm proposed by Reavy and Boncelet is 24%] (19.4%). Under the same condition, the compression improvement ratio of our proposed algorithm over the previous algorithm by Lee and Park is 17.6% and still only has a little encoding time degradation (0.775 s on average). In addition, the encoding time required in the previous free tree-based algorithm is 109.131 s on average while our proposed algorithm takes 0.995 s; the average compression ratio of our proposed TM-based algorithm, 1.60, is quite competitive to that of the free tree-based algorithm, 1.62
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paper_title: Dynamic image data compression in spatial and temporal domains: theory and algorithm

paper_content:

Advanced medical imaging requires storage of large quantities of digitized clinical data. These data must be stored in such a way that their retrieval does not impair the clinician's ability to make a diagnosis. We propose a theory and algorithm for near lossless dynamic image data compression. Taking advantage of domain-specific knowledge related to medical imaging, medical practice and the dynamic imaging modality, a compression ratio greater than 80:1 is achieved. The high compression ratios are achieved by the proposed algorithm through three stages: (1) addressing temporal redundancies in the data through application of image optimal sampling, (2) addressing spatial redundancies in the data through cluster analysis, and (3) efficient coding of image data using standard still-image compression techniques. To illustrate the practicality of the algorithm, a simulated positron emission tomography (PET) study using the fluoro-deoxy-glucose (FDG) tracer is presented. Realistic dynamic image data are generated by virtual scanning of a simulated brain phantom as a real PET scanner. These data are processed using the conventional and proposed algorithms as well as the techniques for storage and analysis. The resulting parametric images obtained from the conventional and proposed approaches are subsequently compared to evaluate the proposed compression algorithm. The storage space for dynamic image data reduced by more than 95%, without loss in diagnostic quality. Therefore, the proposed theory and algorithm are expected to be very useful in medical image database management and telecommunication.
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paper_title: Near-lossless/lossless compression of error-diffused images using a two-pass approach

paper_content:

The current international standard, the Joint Bilevel Image Experts Group (JBIG), is a representative of a bilevel image compression algorithm. It compresses bilevel images with high performance, but it shows relatively low performance in compressing error-diffused halftone images. This paper proposes a new bilevel image compression for error-diffused images, which is based on Bayes' theorem. The proposed coding procedure consists of two passes. It groups 2 x 2 dots into a cell, where each cell is represented by the number of black dots and the locations of the black dots in the cell. The number of black dots in the cell is encoded in the first pass, and their locations are encoded in the second pass. The first pass performs a near-lossless compression, which can be refined to be lossless by the second pass. Experimental results show a high compression performance for the proposed method when it is applied to error-diffused images.
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paper_title: Fast fractal compression of greyscale images.

paper_content:

A new algorithm for fractal compression of greyscale images is presented. It uses some previous results allowing the compression process to be reduced to a nearest neighbors problem, and is essentially based on a geometrical partition of the image block feature space. Experimental comparisons with previously published methods show a significant improvement in speed with no quality loss.
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paper_title: Lossless image compression based on optimal prediction, adaptive lifting, and conditional arithmetic coding

paper_content:

The optimal predictors of a lifting scheme in the general n-dimensional case are obtained and applied for the lossless compression of still images using first quincunx sampling and then simple row-column sampling. In each case, the efficiency of the linear predictors is enhanced nonlinearly. Directional postprocessing is used in the quincunx case, and adaptive-length postprocessing in the row-column case. Both methods are seen to perform well. The resulting nonlinear interpolation schemes achieve extremely efficient image decorrelation. We further investigate context modeling and adaptive arithmetic coding of wavelet coefficients in a lossless compression framework. Special attention is given to the modeling contexts and the adaptation of the arithmetic coder to the actual data. Experimental evaluation shows that the best of the resulting coders produces better results than other known algorithms for multiresolution-based lossless image coding.
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paper_title: Compound image compression for real-time computer screen image transmission

paper_content:

We present a compound image compression algorithm for real-time applications of computer screen image transmission. It is called shape primitive extraction and coding (SPEC). Real-time image transmission requires that the compression algorithm should not only achieve high compression ratio, but also have low complexity and provide excellent visual quality. SPEC first segments a compound image into text/graphics pixels and pictorial pixels, and then compresses the text/graphics pixels with a new lossless coding algorithm and the pictorial pixels with the standard lossy JPEG, respectively. The segmentation first classifies image blocks into picture and text/graphics blocks by thresholding the number of colors of each block, then extracts shape primitives of text/graphics from picture blocks. Dynamic color palette that tracks recent text/graphics colors is used to separate small shape primitives of text/graphics from pictorial pixels. Shape primitives are also extracted from text/graphics blocks. All shape primitives from both block types are losslessly compressed by using a combined shape-based and palette-based coding algorithm. Then, the losslessly coded bitstream is fed into a LZW coder. Experimental results show that the SPEC has very low complexity and provides visually lossless quality while keeping competitive compression ratios.
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paper_title: Image compression by cellular neural networks

paper_content:

This paper demonstrates how the cellular neural-network universal machine (CNNUM) architecture can be applied to image compression. We present a spatial subband image-compression method well suited to the local nature of the CNNUM. In case of lossless image compression, it outperforms the JPEG image-compression standard both in terms of compression efficiency and speed. It performs especially well with radiographical images (mammograms); therefore, it is suggested to use it as part of a cellular neural/nonlinear (CNN)-based mammogram-analysis system. This paper also gives a CNN-based method for the fast implementation of the moving pictures experts group (MPEG) and joint photographic experts group (JPEG) moving and still image-compression standards.
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paper_title: Tiling and adaptive image compression

paper_content:

We investigate the task of compressing an image by using different probability models for compressing different regions of the image. In this task, using a larger number of regions would result in better compression, but would also require more bits for describing the regions and the probability models used in the regions. We discuss using quadtree methods for performing the compression. We introduce a class of probability models for images, the k-rectangular tilings of an image, that is formed by partitioning the image into k rectangular regions and generating the coefficients within each region by using a probability model selected from a finite class of N probability models. For an image of size n/spl times/n, we give a sequential probability assignment algorithm that codes the image with a code length which is within O(k log(Nn/k) of the code length produced by the best probability model in the class. The algorithm has a computational complexity of O(Nn/sup 3/). An interesting subclass of the class of k-rectangular tilings is the class of tilings using rectangles whose widths are powers of two. This class is far more flexible than quadtrees and yet has a sequential probability assignment algorithm that produces a code length that is within O(k log(Nn/k) of the best model in the class with a computational complexity of O(Nn/sup 2/logn) (similar to the computational complexity of sequential probability assignment using quadtrees). We also consider progressive transmission of the coefficients of the image.
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paper_title: An algorithm for compression of bilevel images.

paper_content:

This paper presents the block arithmetic coding for image compression (BACIC) algorithm: a new method for lossless bilevel image compression which can replace JBIG, the current standard for bilevel image compression. BACIC uses the block arithmetic coder (BAC): a simple, efficient, easy-to-implement, variable-to-fixed arithmetic coder, to encode images. BACIC models its probability estimates adaptively based on a 12-bit context of previous pixel values; the 12-bit context serves as an index into a probability table whose entries are used to compute p(1) (the probability of a bit equaling one), the probability measure BAC needs to compute a codeword. In contrast, the Joint Bilevel Image Experts Group (JBIG) uses a patented arithmetic coder, the IBM QM-coder, to compress image data and a predetermined probability table to estimate its probability measures. JBIG, though, has not get been commercially implemented; instead, JBIG's predecessor, the Group 3 fax (G3), continues to be used. BACIC achieves compression ratios comparable to JBIG's and is introduced as an alternative to the JBIG and G3 algorithms. BACIC's overall compression ratio is 19.0 for the eight CCITT test images (compared to JBIG's 19.6 and G3's 7.7), is 16.0 for 20 additional business-type documents (compared to JBIG's 16.0 and G3's 6.74), and is 3.07 for halftone images (compared to JBIG's 2.75 and G3's 0.50).
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paper_title: Dictionary Design for Text Image Compression with JBIG2

paper_content:

The JBIG2 standard for lossy and lossless bilevel image coding is a very flexible encoding strategy based on pattern matching techniques. This paper addresses the problem of compressing text images with JBIG2. For text image compression, JBIG2 allows two encoding strategies: SPM and PM&S. We compare in detail the lossless and lossy coding performance using the SPM-based and PM&S-based JBIG2, including their coding efficiency, reconstructed image quality and system complexity. For the SPM-based JBIG2, we discuss the bit rate tradeoff associated with symbol dictionary design. We propose two symbol dictionary design techniques: the class-based and tree-based techniques. Experiments show that the SPM-based JBIG2 is a more efficient lossless system, leading to 8% higher compression ratios on average. It also provides better control over the reconstructed image quality in lossy compression. However, SPM's advantages come at the price of higher encoder complexity. The proposed class-based and tree-based symbol dictionary designs outperform simpler dictionary formation techniques by 8% for lossless and 16-18% for lossy compression.
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paper_title: High performance scalable image compression with EBCOT

paper_content:

A new image compression algorithm is proposed, based on independent embedded block coding with optimized truncation of the embedded bit-streams (EBCOT). The algorithm exhibits state-of-the-art compression performance while producing a bit-stream with a rich feature set, including resolution and SNR scalability together with a random access property. The algorithm has modest complexity and is extremely well suited to applications involving remote browsing of large compressed images. The algorithm lends itself to explicit optimization with respect to MSE as well as more realistic psychovisual metrics, capable of modeling the spatially varying visual masking phenomenon.
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paper_title: Fuzzy vector quantization algorithms and their application in image compression.

paper_content:

This paper presents the development and evaluation of fuzzy vector quantization algorithms. These algorithms are designed to achieve the quality of vector quantizers provided by sophisticated but computationally demanding approaches, while capturing the advantages of the frequently used in practice k-means algorithm, such as speed, simplicity, and conceptual appeal. The uncertainty typically associated with clustering tasks is formulated in this approach by allowing the assignment of each training vector to multiple clusters in the early stages of the iterative codebook design process. A training vector assignment strategy is also proposed for the transition from the fuzzy mode, where each training vector can be assigned to multiple clusters, to the crisp mode, where each training vector can be assigned to only one cluster. Such a strategy reduces the dependence of the resulting codebook on the random initial codebook selection. The resulting algorithms are used in image compression based on vector quantization. This application provides the basis for evaluating the computational efficiency of the proposed algorithms and comparing the quality of the resulting codebook design with that provided by competing techniques.
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paper_title: Pre-post quantisation and integer wavelet for image compression

paper_content:

Quantisation prior to the integer wavelet transform (Pre-Qnt) is introduced. The best combination of the Pre-Qnt and the conventional post-quantisation (Post-Qnt) in multi-stage band splitting based on the integer wavelet transform is presented.
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paper_title: Image compression based on fuzzy algorithms for learning vector quantization and wavelet image decomposition

paper_content:

This paper evaluates the performance of an image compression system based on wavelet-based subband decomposition and vector quantization. The images are decomposed using wavelet filters into a set of subbands with different resolutions corresponding to different frequency bands. The resulting subbands are vector quantized using the Linde-Buzo-Gray (1980) algorithm and various fuzzy algorithms for learning vector quantization (FALVQ). These algorithms perform vector quantization by updating all prototypes of a competitive neural network through an unsupervised learning process. The quality of the multiresolution codebooks designed by these algorithms is measured on the reconstructed images belonging to the training set used for multiresolution codebook design and the reconstructed images from a testing set.
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paper_title: Linear mixture analysis-based compression for hyperspectral image analysis

paper_content:

Due to significantly improved spectral resolution produced by hyperspectral sensors, the band-to-band correlation is generally very high and can be removed without loss of crucial information. Data compression is an effective means to eliminate such redundancy resulting from high interband correlation. In hyperspectral imagery, various information comes from different signal sources, which include man-made targets, natural backgrounds, unknown clutters, interferers, unidentified anomalies, etc. In many applications, whether or not a compression technique is effective is measured by the degree of information loss rather than information recovery. For example, compression of noise or interferers is highly desirable to image analysis and interpretation. In this paper, we present an unsupervised fully constrained least squares (UFCLS) linear spectral mixture analysis (LSMA)-based compression technique for hyperspectral target detection and classification. Unlike most compression techniques, which deal directly with grayscale images, the proposed compression approach generates and encodes the fractional abundance images of targets of interest present in an image scene to achieve data compression. Since the vital information used for image analysis is generally preserved and retained in these fractional abundance images, the loss of information may have little impact on image analysis. On some occasions, it even improves performance analysis. Airborne Visible/InfraRed Imaging Spectrometer (AVIRIS) and Hyperspectral Digital Imagery Collection Experiment (HYDICE) data are used for experiments to evaluate our proposed LSMA-based compression technique used for applications in hyperspectral detection and image classification. The classification results using the original data and the UFCLS-decompressed data are shown to be very close with no visible difference. But a compression ratio for the HYDICE data with water bands removed can achieve as high as 138:1 with peak SNR (PSNR) 33 dB, while a compression ratio of the AVIRIS scene also with water bands removed is 90:1 with PSNR 40 dB.
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paper_title: Image compression via improved quadtree decomposition algorithms.

paper_content:

Quadtree decomposition is a simple technique used to obtain an image representation at different resolution levels. This representation can be useful for a variety of image processing and image compression algorithms. This paper presents a simple way to get better compression performances (in MSE sense) via quadtree decomposition, by using near to optimal choice of the threshold for quadtree decomposition; and bit allocation procedure based on the equations derived from rate-distortion theory. The rate-distortion performance of the improved algorithm is calculated for some Gaussian field, and it is examined vie simulation over benchmark gray-level images. In both these cases, significant improvement in the compression performances is shown.
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paper_title: Compression of multispectral remote sensing images using clustering and spectral reduction

paper_content:

Image compression has been one of the main research topics in the field of image processing for a long time. The research usually focuses on compressing images that are visible to humans. The images being compressed are usually gray-level images or RGB color images. Recent advances in technology, however, enable the authors to make the detailed processing of spectral features in the images. Therefore, the compression of images with many spectral channels, called multispectral images, is required. Many methods used in traditional lossy image compression can be reused also in the compression of multispectral images. In this paper, a new combination of clustering spectra, manipulating spectral vectors, and encoding and decoding for multispectral images is presented. In the manipulation of the spectral vectors PCA, ICA, and wavelets are used. The approach is based on extracting relevant spectral information. Furthermore, some quantitative quality measures for multispectral images are presented.
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paper_title: New image compression techniques using multiwavelets and multiwavelet packets

paper_content:

Advances in wavelet transforms and quantization methods have produced algorithms capable of surpassing the existing image compression standards like the Joint Photographic Experts Group (JPEG) algorithm. For best performance in image compression, wavelet transforms require filters that combine a number of desirable properties, such as orthogonality and symmetry. However, the design possibilities for wavelets are limited because they cannot simultaneously possess all of the desirable properties. The relatively new field of multiwavelets shows promise in obviating some of the limitations of wavelets. Multiwavelets offer more design options and are able to combine several desirable transform features. The few previously published results of multiwavelet-based image compression have mostly fallen short of the performance enjoyed by the current wavelet algorithms. This paper presents new multiwavelet transform and quantization methods and introduces multiwavelet packets. Extensive experimental results demonstrate that our techniques exhibit performance equal to, or in several cases superior to, the current wavelet filters.
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paper_title: Image data compression using cubic convolution spline interpolation

paper_content:

A new cubic convolution spline interpolation (CCSI )for both one-dimensional (1-D) and two-dimensional (2-D) signals is developed in order to subsample signal and image compression data. The CCSI yields a very accurate algorithm for smoothing. It is also shown that this new and fast smoothing filter for CCSI can be used with the JPEG standard to design an improved JPEG encoder-decoder for a high compression ratio.
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paper_title: Flexible, highly scalable, object-based wavelet image compression algorithm for network applications

paper_content:

The authors propose a highly scalable image compression scheme based on the set partitioning in hierarchical trees (SPIHT) algorithm. The proposed algorithm, called highly scalable SPIHT (HS-SPIHT), adds the spatial scalability feature to the SPIHT algorithm through the introduction of multiple resolution-dependent lists and a resolution-dependent sorting pass. It keeps the important features of the original SPIHT algorithm such as compression efficiency, full SNR scalability and low complexity. The flexible bitstream of the HS-SPIHT encoder can easily be adapted to various resolution requirements at any bit rate. The parsing process can be carried out on-the-fly without decoding the bitstream by a simple parser (transcoder) that forms a part of a smart network. The HS-SPIHT algorithm is further developed for fully scalable coding of arbitrarily shaped visual objects. The proposed highly scalable algorithm finds applications in progressive web browsing, visual databases and especially in image transmission over heterogeneous networks.
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paper_title: Compression of Multispectral Images by Three-Dimensional SPIHT Algorithm

paper_content:

The authors carry out low bit-rate compression of multispectral images by means of the Said and Pearlman's SPIHT algorithm, suitably modified to take into account the interband dependencies. Two techniques are proposed: in the first, a three-dimensional (3D) transform is taken (wavelet in the spatial domain, Karhunen-Loeve in the spectral domain) and a simple 3D SPIHT is used; in the second, after taking a spatial wavelet transform, spectral vectors of pixels are vector quantized and a gain-driven SPIHT is used. Numerous experiments on two sample multispectral images show very good performance for both algorithms.
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paper_title: Improved image compression using S-tree and shading approach

paper_content:

Distasi et al. (see ibid., vol.45, p.1095-1100, 1997), presented a storage-saving image compression method called the B-tree triangular coding (BTTC) method. Based on the modified S-tree data structure and the Gouraud shading method, this paper presents an improved image compression method railed the S-tree compression (STC) method. Experimental results illustrate that the bit rates and the image quality of the proposed STC method are quite competitive with the BTTC method, Due to the simple geometrical decomposition in the STC method, the ratio of the execution time or the proposed method over the BTTC method is less than 1/2.
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paper_title: An efficient BTC image compression technique

paper_content:

This paper presents a moment preserving and visual information dominance technique to achieve the low-bit rate block truncation coding (BTC). Compared with other existing strategies as transform coding and vector quantization, conventional BTC compression has the advantage of simple and fast computation. Nevertheless the compression ratio is limited by its low efficiency. Our proposed technique accomplishes the goal of simple computation with variable bit rate selection by the moment preservation and information extraction algorithm. The proposed technique has the advantage of simple operations and it does not require complicated mathematical computations. Thus, the overall computation does not increase the burden compared with ordinary BTC. The simulations are carried with natural images to evaluate the performance. The generated decoded images have moderate quality with a bit rate of 0.5-1.0 bit/pixel.
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paper_title: Very low bit rate color image compression by using stack-run-end coding

paper_content:

A low complexity wavelet based image coding algorithm for color image compression is presented. The key renovation of this algorithm is that a small number of symbol set was designed to convert the information from the wavelet transform domain into a compact data structure for each subband. The scheme works first by color space conversion, followed by uniform scalar quantization and data conversion where raster scanning order is performed for individual subband and quantized coefficients are converted into the symbol stream according to the designed symbol representation. Two different context lists in each subband are specified from the symbol stream and alternatively compressed by an adaptive arithmetic coder with high efficiency. Unlike zerotree coding or its variations which utilize the intersubband relationship into its own data representation, our work is a low complexity intrasubband based coding method which only addresses the information within the subband. The only extension is the termination symbols which carry the zero value information towards the end of the subband or across the subbands till the end of the image. Compared with the zerotree refined schemes, this algorithm results in competitive PSNR values and perceptually high quality images at the same compression ratio for color image compression.
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paper_title: Novel fuzzy reinforced learning vector quantisation algorithm and its application in image compression

paper_content:

A new approach to the design of optimised codebooks using vector quantisation (VQ) is presented. A strategy of reinforced learning (RL) is proposed which exploits the advantages offered by fuzzy clustering algorithms, competitive learning and knowledge of training vector and codevector configurations. Results are compared with the performance of the generalised Lloyd algorithm (GLA) and the fuzzy K-means (FKM) algorithm. It has been found that the proposed algorithm, fuzzy reinforced learning vector quantisation (FRLVQ), yields an improved quality of codebook design in an image compression application when FRLVQ is used as a pre-process. The investigations have also indicated that RL is insensitive to the selection of both the initial codebook and a learning rate control parameter, which is the only additional parameter introduced by RL from the standard FKM.
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paper_title: Lossless image compression with multiscale segmentation

paper_content:

This paper is concerned with developing a lossless image compression method which employs an optimal amount of segmentation information to exploit spatial redundancies inherent in image data. Multiscale segmentation is obtained using a previously proposed transform which provides a tree-structured segmentation of the image into regions characterized by grayscale homogeneity. In the proposed algorithm we prune the tree to control the size and number of regions thus obtaining a rate-optimal balance between the overhead inherent in coding the segmented data and the coding gain that we derive from it. Another novelty of the proposed approach is that we use an image model comprising separate descriptions of pixels lying near the edges of a region and those lying in the interior. Results show that the proposed algorithm can provide performance comparable to the best available methods and 15-20% better compression when compared with the JPEG lossless compression standard for a wide range of images.
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paper_title: New efficient methods of image compression in digital cameras with color filter array

paper_content:

Many consumer digital color cameras use a single light sensitive sensor and a color filter array (CFA) with each pixel element recording intensity information of one color component. The captured data is interpolated into a full color image, which is then compressed in many applications. Carrying out color interpolation before compression introduces redundancy in the data. In this paper we discuss methods and issues involved in the compression of CFA data before full color interpretation. The compression methods described operate on the same number of pixels as the sensor data. To obtain improved image quality, median filtering is applied as post-processing. Furthermore, to assure low complexity, the CFA data is compressed by JPEG. Simulations have demonstrated that substantial improvement in image quality is achievable using these new schemes.
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paper_title: Medical image compression by sampling DCT coefficients

paper_content:

Advanced medical imaging requires storage of large quantities of digitized clinical data. Due to the constrained bandwidth and storage capacity, however, a medical image must be compressed before transmission and storage. Among the existing compression schemes, transform coding is one of the most effective strategies. Image data in the spatial domain is transformed into the spectral domain after the transformation to attain more compression gains. Based on the quantization strategy, coefficients of low amplitude in the transformed domain are discarded and significant coefficients are preserved to increase the compression ratio without inducing salient distortion. We use an adaptive sampling algorithm by calculating the difference area between correct points and predicted points to decide the significant coefficients. Recording or transmitting the significant coefficients instead of the whole coefficients achieves the goal of compression. On the decoder side, a linear equation is employed to reconstruct the coefficients between two sequent significant coefficients. Simulations are carried out to different medical images, which include sonogram, angiogram, computed tomography, and X-ray images.
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paper_title: Integer Wavelet Transform for Embedded Lossy to Lossless Image Compression

paper_content:

The use of the discrete wavelet transform (DWT) for embedded lossy image compression is now well established. One of the possible implementations of the DWT is the lifting scheme (LS). Because perfect reconstruction is granted by the structure of the LS, nonlinear transforms can be used, allowing efficient lossless compression as well. The integer wavelet transform (IWT) is one of them. This is an interesting alternative to the DWT because its rate-distortion performance is similar and the differences can be predicted. This topic is investigated in a theoretical framework. A model of the degradations caused by the use of the IWT instead of the DWT for lossy compression is presented. The rounding operations are modeled as additive noise. The noise are then propagated through the LS structure to measure their impact on the reconstructed pixels. This methodology is verified using simulations with random noise as input. It predicts accurately the results obtained using images compressed by the well-known EZW algorithm. Experiment are also performed to measure the difference in terms of bit rate and visual quality. This allows to a better understanding of the impact of the IWT when applied to lossy image compression.
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paper_title: Lossless image compression using ordered binary-decision diagrams

paper_content:

A lossless compression algorithm for images based on ordered binary-decision diagrams (OBDDs) is presented. The algorithm finds an OBDD which represents the image exactly and then codes the OBDD efficiently. The results obtained show a great improvement with respect to a previous work.
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paper_title: Hadamard-based image decomposition and compression

paper_content:

We develop a general algorithm for decomposition and compression of grayscale images. The decomposition can be expressed as a functional relation between the original image and the Hadamard waveforms. The dynamic adaptive clustering procedure incorporates potential functions as a similarity measure for clustering as well as a reclustering phase. The latter is a multi-iteration, convergent procedure which divides the inputs into nonoverlapping clusters. These two techniques allow us to efficiently store and transmit a class of half-tone medical images such as magnetic resonance imaging (MRI) of the human brain. Due to the redundant image structure of MRI, obtained after the decomposition and clustering, almost half of the image can be omitted all together. Naturally, the compression rates for this specific type of grayscale image are increased greatly. A run-length coding is performed in order to compress further the retained information from the first two steps. Although all the techniques applied are simple, they represent an efficient way to compress grayscale images. The algorithm exhibits a performance which is competitive and often outperforming some of the methods reported in the literature.
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Title: Image Compression: A Survey

Section 1: INTRODUCTION
Description 1: This section will introduce the concept of image compression, its importance, and a brief overview of the types of compression methods (lossy and lossless) along with their applications. It will also highlight the scope and goals of the survey.

Section 2: COMPONENTS OF COMPRESSION
Description 2: This section will discuss the fundamental components of compression, focusing on reducing redundancy and irrelevancy in images. It will introduce various image compression algorithms and their effectiveness.

Section 3: RESULTS AND DISCUSSION
Description 3: This section will provide an in-depth analysis of various image compression methods including fractal image compression, region-based heuristic search compression, and low complexity fractal-based compression. It will discuss the results obtained through different techniques and their computational efficiencies.

Section 4: DISCRETE COSINE TRANSFORM
Description 4: This section will explore the discrete cosine transform (DCT) method for image compression. It will cover various approaches and advancements within DCT-based compression techniques, particularly their application in medical image compression.

Section 5: IMAGE COMPRESSION USING CODING OF WAVELET COEFFICIENTS
Description 5: This section will delve into wavelet-based compression techniques, including Embedded Zero Wavelet (EZW) algorithms, hybrid coding, and other wavelet-based methods for efficient image compression.

Section 6: EFFICIENT SPATIAL PREDICTION BASED IMAGE COMPRESSION
Description 6: This section will describe efficient spatial prediction methods for image compression, particularly focusing on techniques suitable for multimedia applications and wireless transmission.

Section 7: EDGE-BASED INPAINTING COMPRESSION
Description 7: This section will discuss edge-based inpainting techniques for lossy compression, highlighting methods that utilize visual redundancy removal for efficient compression with high visual quality.

Section 8: LOSSLESS IMAGE COMPRESSION ALGORITHM
Description 8: This section will cover various lossless image compression algorithms, their mechanisms, and their positive aspects. It will detail different techniques and their application, particularly in the field of medical imagery.

Section 9: COMPOUND REAL TIME IMAGE COMPRESSION
Description 9: This section will review recent studies on compound image compression, focusing on the SPEC algorithm, its methodology, and its experimental results.

Section 10: IMAGE COMPRESSION BY CELLULAR NEURAL NETWORKS (CNNUM)
Description 10: This section will present various algorithms based on Cellular Neural Networks (CNNUM) for fast compression of still and moving images, along with performance analysis.

Section 11: COMPRESSION BASED ON FUZZY ALGORITHMS
Description 11: This section will explore image compression techniques using fuzzy algorithms, discussing their principles, application, and performance metrics.

Section 12: EFFICIENT COMPRESSION ALGORITHM FOR HYPER SPECTRAL IMAGES
Description 12: This section will examine efficient compression algorithms specifically designed for hyper-spectral images, including techniques such as adaptive 3D-DWT and asymmetric zero tree.

Section 13: SPIHT ALGORITHM
Description 13: This section will detail the SPIHT (Set Partitioning in Hierarchical Trees) algorithm and its variations, highlighting their strengths and application areas, particularly in compressing multispectral images.

Section 14: IMAGE COMPRESSION SYSTEM USING DATA RE-ORDERING TECHNIQUE
Description 14: This section will introduce the system of image compression using data re-ordering techniques, its alternative to context modeling, and the benefits it brings in terms of reduced complexity.

Section 15: COLOR IMAGE COMPRESSION
Description 15: This section will discuss color image compression methods, focusing on progressive coding of color information and techniques to retain original image quality.

Section 16: HADAMARD BASED IMAGE DECOMPOSITION AND COMPRESSION
Description 16: This section will present image compression methods based on Hadamard transforms, describing how these techniques improve encoding performance while maintaining image quality.

Section 17: CONCLUSION
Description 17: This section will summarize the survey findings, highlighting the merits, demerits, and applications of the various image compression techniques discussed. It will also discuss the need for further research and advancements in the field.