This monograph introduces a comprehensive framework for optimizing perceptual quality in online, real-time, interactive multimedia systems involving multiple users, performance metrics, and application controls. It outlines an integrated offline-online process that learns perceptual quality under controlled conditions and adapts it to dynamic, real-time environments. The optimization is modeled as a decomposable multi-metric, multi-control problem, solvable in polynomial time by integrating solutions from simpler subproblems. Each subproblem is evaluated using a novel, function-free method based on dominance and a binary-divide algorithm with guaranteed error tolerance.
The work is the first to show that the relationship between a stimulus and its variation in multimedia and psychophysical applications can be nonlinear yet monotonic and non-smooth. It also pioneers a method for optimizing perceptual quality in real-time communications without requiring original data at the receiver. This long-standing open problem is challenging due to the subjective nature of perceptual quality and the complexity of unknown tradeoffs among quality measures. The findings demonstrate the feasibility of solving these challenges through decomposition and dominance, offering practical solutions for improving perceptual quality in online real-time interactive multimedia applications.
Benjamin W. Wah is an Emeritus Professor at The Chinese University of Hong Kong (CUHK) and the Franklin W. Woeltge Emeritus Professor of Electrical and Computer Engineering at the University of Illinois at Urbana-Champaign (UIUC), USA. He previously served as Provost and Wei Lun Professor of Computer Science and Engineering at CUHK, Chair of the Hong Kong Research Grants Council, and Franklin W. Woeltge Professor at UIUC. He received his Ph.D. in Computer Science from the University of California, Berkeley.
Professor Wah’s contributions span big data, multimedia, artificial intelligence, and computer systems, earning him numerous prestigious honors. These include the IEEE CS Technical Achievement Award, IEEE Millennium Medal, W. Wallace-McDowell Award, Richard E. Merwin Award, Tsutomu Kanai Award, the Distinguished Alumni Award from UC Berkeley, and the Bronze Bauhinia Star (Hong Kong). From 2013 to 2018, he served as Chief Scientist of China’s 973 Project on “Theory and Applications of Network Big Data Computations.”
His editorial leadership includes co-founding the IEEE Transactions on Knowledge and Data Engineering and serving as its Editor-in-Chief (1993–1996). He is currently co-Editor-in-Chief of Computers and Education: Artificial Intelligence (Elsevier) and Honorary Editor-in-Chief of Knowledge and Information Systems. He also serves on editorial boards of several journals, including Information Sciences, World Wide Web, and Journal of Computer Science and Technology. A dedicated leader in the IEEE Computer Society, he served as Vice President for Publications (1998–1999) and President (2001). He is a Fellow of the AAAS, ACM, and IEEE.
Jingxi Xu is the Co-Founder and Director of DreamTech. He earned his Ph.D. from the Chinese University of Hong Kong, specializing in multimedia networking and QoE optimization. Jingxi subsequently joined Tencent, where he made significant contributions to the network transmission core for WeMeet. Currently, he is dedicated to developing a high-efficiency general platform for 3D Al-generated content (AlGC), aiming to revolutionize how digital content is created and consumed.
