Video quality assessment based on motion models
Kalpana Seshadrinathan · Texas ScholarWorks (Texas Digital Library) · 2008
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 x