主 講 人：鄺得互教授

地　　點(diǎn)：祥聯(lián)廳

主 辦 方：物理與信息工程學(xué)院

開(kāi)始時(shí)間：2019-01-18 09:00

報(bào)告人簡(jiǎn)介

鄺得互教授,，獲得紐約州立大學(xué)學(xué)士學(xué)位,，滑鐵盧大學(xué)電氣工程碩士學(xué)位，德國(guó)哈根大學(xué)博士學(xué)位?，F(xiàn)為香港城市大學(xué)計(jì)算機(jī)科學(xué)系教授,、系主任。鄺得互教授因在智能計(jì)算及視頻編碼等領(lǐng)域的貢獻(xiàn)而當(dāng)選為IEEE Fellow,?！　∴椊淌谝丫幹盘?hào)處理和優(yōu)化算法理論專(zhuān)著3部，專(zhuān)著圖書(shū)章節(jié)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(guó)際權(quán)威期刊上發(fā)表SCI學(xué)術(shù)論文100余篇,，重要學(xué)術(shù)會(huì)議120余篇，Google Scholar論文引用次數(shù)超過(guò)9000次,。鄺教授擔(dān)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(huì)成員的一名，他曾擔(dān)任過(guò)50個(gè)重要會(huì)議委員會(huì)委員,。同時(shí)鄺教授由于積極推動(dòng)學(xué)術(shù)交流和活動(dòng)而獲得IEEE SMC society最佳分會(huì)主席獎(jiǎng),。

報(bào)告主要內(nèi)容簡(jiǎn)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.