New approaches and a subjective database for video quality assessment

Video quality assessment plays an important role in multimedia systems that process digital images/videos such as video codec, video streaming server. The use of video quality assessment algorithm helps optimize system parameters, increase quality of service, and satisfy customers' demands. Traditional method that recruits human subjects to judge video quality often comes with the expense of time, money, and effort while objective method, which uses computer and built-in algorithms to judge video quality, offers a more affordable way. This dissertation report provides an efficient approach to develop objective video quality assessment algorithm.Algorithms in video quality assessment aim to predict quality of videos in a manner that agrees with subjective ratings of quality judged by human subjects. From that, two important factors are required for the research of video quality assessment. The first factor is an algorithm that is able to predict video quality. Our approach to develop such an algorithm bases on the analyses of spatial and spatiotemporal slices in two separate stages. The first stage estimates perceived quality degradation due to spatial distortion; this stage operates by adaptively applying our previous image quality assessment algorithm on a frame basis with an extension to account for temporal masking. The second stage estimates perceived quality degradation due to joint spatial and temporal distortion; this stage operates by measuring the dissimilarity between the two-dimensional spatiotemporal slices created by taking time-based slices of the original and distorted videos. The combination of these two estimates serves as an overall estimate of perceived quality degradation.The second important factor in the research of video quality assessment is a video-quality database with collected subjective ratings used to validate the algorithms' performance. We create our own video-quality database that consists of more videos ($216$ videos) and more distortion types (six) comparing to the currently available video-quality databases. The experiment to collect subjective ratings of quality is conducted by 40 different subjects following the SAMVIQ methodology.Acknowledge that in many applications, the original video is not available; we develop another video quality assessment algorithm that can predict quality of a processed video without information of the original video. This algorithm, specifically designed for videos compressed by Motion JPEG2000 compression standard, consists of two analyses of quality degradation in the edge/near-edge regions and the non-edge regions of the videos. The algorithm shows promise in the first step of developing a general no-reference algorithm for video quality assessment