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题目： Perceptual Coding: Hype or Hope?
报告人： C.-C. Jay Kuo教授（美国南加州大学多媒体通讯实验室主任）
时间： 2016年3月22日（星期五），上午10:00 – 11:00
邀请人： 王瀚漓 教授
Dr. C.-C. Jay Kuo received his Ph.D. degree from the Massachusetts Institute of Technology in 1987. He is now with the University of Southern California (USC) as Director of the Media Communications Laboratory and Dean’s Professor in Electrical Engineering-Systems. His research interests are in the areas of digital media processing, compression, communication and networking technologies. Dr. Kuo was the Editor-in-Chief for the IEEE Trans. on Information Forensics and Security in 2012-2014. He was the Editor-in-Chief for the Journal of Visual Communication and Image Representation in 1997-2011, and served as Editor for 10 other international journals. Dr. Kuo received the National Science Foundation Young Investigator Award (NYI) and Presidential Faculty Fellow (PFF) Award in 1992 and 1993, respectively. He was an IEEE Signal Processing Society Distinguished Lecturer in 2006, and the recipient of the Electronic Imaging Scientist of the Year Award in 2010 and the holder of the 2010-2011 Fulbright-Nokia Distinguished Chair in Information and Communications Technologies. Dr. Kuo is a Fellow of AAAS, IEEE and SPIE. Dr. Kuo has guided 130 students to their Ph.D. degrees and supervised 25 postdoctoral research fellows. He is a co-author of about 230 journal papers, 870 conference papers and 13 books.
There has been a significant progress in image/video coding in the last 50 years, and many visual coding standards have been established, including JPEG, MPEG-1, MPEG-2, H.264/AVC and H.265, in the last three decades. The visual coding research field has reached a mature stage, and the question “is there anything left for image/video coding?” arises in recent years. One emerging R&D topic is “perceptual coding”. That is, we may leverage the characteristics of the human visual system (HVS) to achieve a higher coding gain. For example, we may change the traditional quality/distortion measure (i.e., PSNR/MSE) to a new perceptual quality/distortion measure and take visual saliency and spatial-temporal masking effects into account. Recent developments in this area will be reviewed first. However, “is this sufficient to keep visual coding research vibrant and prosperous for another decade with such a modification?” The answer is probably not. Instead, I will present a new HVS-centric coding framework that is dramatically differently from the past. This framework is centered on two key concepts – the stair quality function (SQF) and the Just-Noticeable-Differences (JND). It will lead to numerous new R&D opportunities and revolutionize coding research with modern machine learning tools.