主 讲 人：：邝得互教授

地　　点：：祥联厅

主 办 方：：物理与信息工程学院

最先时间：：2019-01-18 09:00

报告人简介

邝得互教授，，获得纽约州立大学学士学位，，滑铁卢大学电气工程硕士学位，，德国哈根大学博士学位。。现为香港城市大学盘算机科学系教授、、、系主任。。邝得互教授因在智能盘算及视频编码等领域的孝顺而中选为IEEE Fellow。。　　邝教授已编著信号处理和优化算法理论专著3部，，专著图书章节9部，，在IEEE Trans. Industrial Electronics, IEEE Trans. Evolutionary Computation, IEEE Trans. Image Process, IEEE Trans. Circuits Syst. Video Technol., Pattern Recognition等国际权威期刊上揭晓SCI学术论文100余篇，，主要学术聚会120余篇，，Google Scholar论文引用次数凌驾9000次。。邝教授担当IEEE Transactions on Industrial Electronics, IEEE Transactions on Industrial Informatics以及elsvier的Information Sciences等期刊的副主编，，作为IEEE SMC理事会成员的一名，，他曾担当过50个主要聚会委员会委员。。同时邝教授由于起劲推动学术交流和活动而获得IEEE SMC society最佳分会主席奖。。

报告主要内容简介

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.