Study of the effect of Sharpness on Blind Video Quality Assessment

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.