Prof. KWONG Tak Wu Sam, Lingnan University Hong Kong, China
Fellow of Hong Kong AES, US NAI and IEEE

香港岭南大学协理副校长邝得互教授在进化算法、人工智能解决方案和图形 / 视频编码的领域钻研多年,涵盖计算智能学、工程、电信、自动化与控制系统等研究范畴。他先后于 2022 年及 2023 年当选 “全球最高被引研究人员”;自 2021 年起跻身美国斯坦福大学 “全球前 2% 顶尖科学家”;并当选Research.com 2024 年度最新版学术分项领域排名中,计算机科学的顶尖科学家。由他领导研发以 AI 技术增强低亮度环境的影像素质,早前在第 49 届日内瓦国际发明展荣膺银奖。

 

Sam Kwong received his B.Sc. degree from the State University of New York at Buffalo, M.A.Sc. in electrical engineering from the University of Waterloo in Canada, and Ph.D. from Fernuniversität Hagen, Germany. Before joining Lingnan University, he was the Chair Professor at the City University of Hong Kong and a Diagnostic Engineer with Control Data Canada. He was responsible for designing diagnostic software to detect the manufacturing faults of the VLSI chips in the Cyber 430 machine. He later joined Bell-Northern Research as a Member of the Scientific Staff working on the Integrated Services Digital Network (ISDN) project.
Kwong is currently Chair Professor at the Lingnan University of the Department of Computing and Decision Science. He previously served as Department Head and Professor from 2012 to 2018 at the City University of Hong Kong. Prof Kwong joined CityU as a Department of Electronic Engineering lecturer in 1989. Prof. Kwong is the associate editor of leading IEEE transaction journals, including IEEE Transactions on Evolutionary Computation, IEEE Transactions on Industrial Informatics, and IEEE Transactions on Cybernetics. He was the President of IEEE Systems, Man And Cybernetics Society from 2022-23.

 

Speech Title: Deep Learning-Based Video Coding and its Applications
Abstract:
In 2016, Cisco released the White paper, VNI Forecast and Methodology 2015-2020, which predicted that by 2020, 82 percent of Internet traffic would come from video applications such as video surveillance and content delivery networks. The report also revealed that in 2015, Internet video surveillance traffic nearly doubled, virtual reality traffic quadrupled, TV grew by 50 percent, and other applications similarly saw significant increases. The report estimated that the annual global traffic would first time exceed the zettabyte (ZB; 1000 exabytes [EB]) threshold in 2016 and will reach 2.3 ZB by 2020, with 1.886 ZB attributed to video data.
Today, AI and machine learning are increasingly being used in video processing to improve video quality, reduce bandwidth requirements, and enhance user experience. For instance, AI algorithms can optimize video encoding parameters based on the content of the video, reducing the bitrate required for a given level of video quality. AI can also be used for video content analysis, enabling automated scene detection, object recognition, and event detection. This has significant applications in video surveillance, where AI algorithms can be used to identify and track individuals or objects of interest in real-time.
Overall, the use of AI in video is a rapidly growing field with immense potential for improving the efficiency and quality of multimedia services. In this talk, I will present the latest research results on machine learning and deep neural network-based video coding, and their applications to the real world, such as saliency detection and underwater imaging.