Open Collections

Xiang, Jie

University of British Columbia

Impressive advancements in capturing, display, and delivery technologies significantly elevate image and video quality, and with that the need for designing new subjective and objective image and video quality metrics as well as coding methods. However, the delivered quality is affected by many factors, such as limited transmission bandwidth and compression distortions, which may cause degradation in image/video quality and in turn reduce the users' quality of experience (QoE). On one hand, technological advances tend to increase consumer expectations, while on the other the explosive use of social media and entertainment playback on a plethora of devices, ranging from Virtual Reality displays to TVs, have given rise to many new challenges in evaluating the quality of the captured and delivered content. As always, service providers would like to have an accurate way of assessing the perceptual quality of the decoded video streams at the receiver end. Although subjective quality assessment is the best way to evaluate delivered content, this is rarely practical for most applications. It is, thus, of great significance to develop effective image and video quality metrics as well as compression schemes, which will address the above-mentioned challenges. In this thesis, we first propose a foveated compression approach for images rendered on head-mounted displays (HMD) for virtual reality (VR) applications and a subjective scheme for measuring the quality of the generated images. Then, we propose deep learning based no-reference quality metrics that evaluate the quality of high-definition (HD) images and videos that have been compressed by the HEVC standard. We have also created comprehensive and representative ground truth datasets that are publicly available and may become a benchmark for research in related areas.

Text; Still Image

2024-05-31

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/84567

Electrical and Computer Engineering

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/