Enhancing Video Coding by Data-driven Techniques and Advanced Models
Abstract
In June 6th 2016, Cisco released the White paper[1], VNI Forecast and Methodology 2015-2020, reported that 82 percent of Internet traffic will come from video applications such as video surveillance, content delivery network, so on by 2020. It also reported that Internet video surveillance traffic nearly doubled, Virtual reality traffic quadrupled, TV grew 50 percent and similar increases for other applications in 2015. The annual global traffic will first time exceed the zettabyte(ZB;1000 exabytes[EB]) threshold in 2016, and will reach 2.3 ZB by 2020. It implies that 1.886ZB belongs to video data. Thus, in order to relieve the burden on video storage, streaming and other video services, researchers from the video community have developed a series of video coding standards. Among them, the most up-to-date is the High Efficiency Video Coding(HEVC) or H.265 standard, which has successfully halved the coding bits of its predecessor, H.264/AVC, without significant increase in perceived distortion. With the rapid growth of network transmission capacity, enjoying high definition video applications anytime and anywhere with mobile display terminals will be a desirable feature in the near future. Due to the lack of hardware computing power and limited bandwidth, lower complexity and higher compression efficiency video coding scheme are still desired. For higher video compression performance, the key optimization problems, mainly decision making and resource allocation problem, shall be solved. In this talk, I will present the most recent research results on machine learning and game theory based video coding. This is very different from the traditional approaches in video coding. We hope applying these intelligent techniques to vide coding could allow us to go further and have more choices in trading off between cost and resources.
SpeakerProf. KWONG Tak Wu Sam | |
Date & Time11 Sep 2017 (Monday) 15:00 - 16:00 | |
VenueE11-1041 (University of Macau) | |
Organized byDepartment of Computer and Information Science |
Biography
Sam Kwong received the B.Sc. degree from the State University of New York at Buffalo, Buffalo, NY, in 1983, the M.A.Sc. degree in electrical engineering from the University of Waterloo, Waterloo, ON, Canada, in 1985, and the Ph.D. degree from the Fernuniversität Hagen, Hagen, Germany, in 1996. From 1985 to 1987, he was a Diagnostic Engineer with Control Data Canada, where he designed the diagnostic software to detect the manufacture faults of the VLSI chips in the Cyber 430 machine. He is the associate editor of the IEEE transactions on Evolutionary Computation, the Associate Editor for the IEEE Transactions on Industrial Informatics, the IEEE Transactions on Industrial Electronics, and the Journal of Information Science. Currently, he is the Head and Professor of the department of Computer Science, City In terms of professional services, he was General Chair of IEEE SMC 2015 and gained the right to host the prestigious IEEE International Conference on Systems, Man, and Cybernetics at CityU on October 9-12, 2015, with nearly 600 scholars from around the world participating. He is also the Vice President for IEEE Systems, Man and Cybernetics for conferences and meetings from 2014 till present. Prof. Kwong was elevated to IEEE fellow for his contributions on Optimization Techniques for Cybernetics and Video coding in 2014. He is also appointed as IEEE Distinguished Lecturer for IEEE SMC society from March 2017. In his more recent work, Prof Kwong has also proposed a number of optimization techniques for video coding, including developing efficient algorithms to address the high computational complexity problems for mode decision and this enables adaptive thresholds to be derived to make intelligent decisions for mode decision and motion estimation, thus significantly reducing the computational complexity in H.264 encoding in a clever way. In addition, this enables the practical implementation of video coder to industry. His innovations have already inspired the publication of a number of top-ranked journal papers (IEEE Transactions on Image Processing, IEEE Transactions on Circuit and Systems for Video Technology, IEEE Transactions on Broadcasting, IEEE Transactions on Multimedia, IEEE Transactions on Industrial Electronics). His discoveries have also been patented in the US from 2011 to 2017.