Perceptual Video Quality Assessment and Enhancement
dc.comment.hidden | Upon transferring copyright to IEEE, authors and/or their companies have the right to post their IEEE-copyrighted material on their own servers without permission, provided that the server displays a prominent notice alerting readers to their obligations with respect to copyrighted material and that the posted work includes an IEEE copyright notice. | en |
dc.contributor.author | Zeng, Kai | |
dc.date.accessioned | 2013-08-21T18:20:20Z | |
dc.date.available | 2013-08-21T18:20:20Z | |
dc.date.issued | 2013-08-21T18:20:20Z | |
dc.date.submitted | 2013-08-12 | |
dc.description.abstract | With the rapid development of network visual communication technologies, digital video has become ubiquitous and indispensable in our everyday lives. Video acquisition, communication, and processing systems introduce various types of distortions, which may have major impact on perceived video quality by human observers. Effective and efficient objective video quality assessment (VQA) methods that can predict perceptual video quality are highly desirable in modern visual communication systems for performance evaluation, quality control and resource allocation purposes. Moreover, perceptual VQA measures may also be employed to optimize a wide variety of video processing algorithms and systems for best perceptual quality. This thesis exploits several novel ideas in the areas of video quality assessment and enhancement. Firstly, by considering a video signal as a 3D volume image, we propose a 3D structural similarity (SSIM) based full-reference (FR) VQA approach, which also incorporates local information content and local distortion-based pooling methods. Secondly, a reduced-reference (RR) VQA scheme is developed by tracing the evolvement of local phase structures over time in the complex wavelet domain. Furthermore, we propose a quality-aware video system which combines spatial and temporal quality measures with a robust video watermarking technique, such that RR-VQA can be performed without transmitting RR features via an ancillary lossless channel. Finally, a novel strategy for enhancing video denoising algorithms, namely poly-view fusion, is developed by examining a video sequence as a 3D volume image from multiple (front, side, top) views. This leads to significant and consistent gain in terms of both peak signal-to-noise ratio (PSNR) and SSIM performance, especially at high noise levels. | en |
dc.identifier.uri | http://hdl.handle.net/10012/7720 | |
dc.language.iso | en | en |
dc.pending | false | en |
dc.publisher | University of Waterloo | en |
dc.subject | Video Quality Assessment | en |
dc.subject | Video Denoising | en |
dc.subject.program | Electrical and Computer Engineering | en |
dc.title | Perceptual Video Quality Assessment and Enhancement | en |
dc.type | Doctoral Thesis | en |
uws-etd.degree | Doctor of Philosophy | en |
uws-etd.degree.department | Electrical and Computer Engineering | en |
uws.peerReviewStatus | Unreviewed | en |
uws.scholarLevel | Graduate | en |
uws.typeOfResource | Text | en |