Video quality assessment based on motion models

A large amount of digital visual data is being distributed and communicated globally and the question of video quality control becomes a central concern. Unlike many signal processing applications, the intended receiver of video signals is nearly always the human eye. Video quality assessment algorithms must attempt to assess perceptual degradations in videos. My dissertation focuses on full reference methods of image and video quality assessment, where the availability of a perfect or pristine reference image/video is assumed. A large body of research on image quality assessment has focused on models of the human visual system. The premise behind such metrics is to process visual data by simulating the visual pathway of the eye-brain system. Recent approaches to image quality assessment, the structural similarity index and information theoretic models, avoid explicit modeling of visual mechanisms and use statistical properties derived from the images to formulate measurements of image quality. I show that the structure measurement in structural similarity is equivalent to contrast masking models that form a critical component of many vision based methods. I also show the equivalence of the structural and the information theoretic metrics under certain assumptions on the statistical distribution of the reference and distorted images. Videos contain many artifacts that are specific to motion and are largely temporal. Motion information plays a key role in visual perception of video signals. I develop a general, spatio-spectrally localized multi-scale framework for evaluating dynamic video fidelity that integrates both spatial and temporal aspects of distortion assessment. Video quality is evaluated in space and time by evaluating motion quality along computed motion trajectories. Using this framework, I develop a full-reference video quality assessment algorithm known as the MOtion-based Video Integrity Evaluation index, or MOVIE index. Lastly, and significantly, I conducted a large-scale subjective study on a database of videos distorted by present generation video processing and communication technology. The database contains 150 distorted videos obtained from 10 naturalistic reference videos and each video was evaluated by 38 human subjects in the study. I study the performance of leading, publicly available objective video quality assessment algorithms on this database.