Examination Committee

Prof Jack C P CHENG, CIVL/HKUST (Chairperson)
Prof Matthew MCKAY, ECE/HKUST (Thesis Supervisor)
Prof Jian ZHANG, School of Computing and Communication, University of Technology Sydney (External Examiner)
Prof Ross MURCH, ECE/HKUST
Prof Xiaopeng FAN, ECE/HKUST
Prof Andrew HORNER, CSE/HKUST

Abstract

The application and use of multimedia signals such as image, video, and sound have increased immensely in daily-life. These visual signals are contaminated with several varieties of distortions during the acquisition, compression, transmission and/or display of the signal on screens. The human vision is the ultimate receiver of these multimedia signals. Consequently, visual perception based image quality assessment (IQA) and just noticeable difference (JND) have become important as they can predict the signal quality and highlight the regions of importance which are compatible with human vision. With this view, in this thesis, we model human vision based on how it perceives information from visual signals. In this thesis, our goal is to integrate the properties of human vision with the statistical properties of visual signals for efficient quality assessment of these signals and JND estimation. The main contributions of this thesis are the proposals of: 1) a new JND estimation algorithm for images using RMS contrast and feed-back mechanism, 2) the first no-reference IQA algorithm for 3D synthesized images using local descriptors, 3) a reduced-reference IQA algorithms for screen content images based upon the prediction, and 4) an application of perceptual IQA metrics in forming a generic image reconstruction algorithm which can be applied to several image/video processing applications.