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主 講 人:鄺得互教授

地  點:祥聯廳

主 辦 方:物理與信息工程學院

開始時間:2019-01-18 09:00

報告人簡介

鄺得互教授,獲得紐約州立大學學士學位,滑鐵盧大學電氣工程碩士學位,德國哈根大學博士學位。現為香港城市大學計算機科學系教授、系主任。鄺得互教授因在智能計算及視頻編碼等領域的貢獻而當選為IEEE Fellow。  鄺教授已編著信號處理和優化算法理論專著3部,專著圖書章節9部,在IEEE Trans. Industrial Electronics, IEEE Trans. Evolutionary Computation, IEEE Trans. Image Process, IEEE Trans. Circuits Syst. Video Technol., Pattern Recognition等國際權威期刊上發表SCI學術論文100余篇,重要學術會議120余篇,Google Scholar論文引用次數超過9000次。鄺教授擔任IEEE Transactions on Industrial Electronics, IEEE Transactions on Industrial Informatics以及elsvier的Information Sciences等期刊的副主編,作為IEEE SMC理事會成員的一名,他曾擔任過50個重要會議委員會委員。同時鄺教授由于積極推動學術交流和活動而獲得IEEE SMC society最佳分會主席獎。

報告主要內容簡介

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.

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