Study of the effect of Sharpness on Blind Video Quality Assessment
Anantha Prabhu David Pratap Narayana Darapeni Anwesh P R
Introduction: Video Quality Assessment (VQA) is one of the important areas of
study in this modern era, where video is a crucial component of communication
with applications in every field. Rapid technology developments in mobile
technology enabled anyone to create videos resulting in a varied range of video
quality scenarios. Objectives: Though VQA was present for some time with the
classical metrices like SSIM and PSNR, the advent of machine learning has
brought in new techniques of VQAs which are built upon Convolutional Neural
Networks (CNNs) or Deep Neural Networks (DNNs). Methods: Over the past years
various research studies such as the BVQA which performed video quality
assessment of nature-based videos using DNNs exposed the powerful capabilities
of machine learning algorithms. BVQA using DNNs explored human visual system
effects such as content dependency and time-related factors normally known as
temporal effects. Results: This study explores the sharpness effect on models
like BVQA. Sharpness is the measure of the clarity and details of the video
image. Sharpness typically involves analyzing the edges and contrast of the
image to determine the overall level of detail and sharpness. Conclusion: This
study uses the existing video quality databases such as CVD2014. A comparative
study of the various machine learning parameters such as SRCC and PLCC during
the training and testing are presented along with the conclusion.