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Deep Learning-Based Video Coding and Its Applications
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Speaker (s):
Sam Kwong
Chair Professor,
Computational intelligence
Lingnan University
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Date:
Time:
Venue:
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14 December 2023, Thursday
4:30pm – 5:30pm
School of Economics/School of Computing
& Information Systems 2 (SOE/SCIS 2)
Level 4, Seminar Room 4-2
Singapore Management University
90 Stamford Road, Singapore 178903
Please register by 13 December 2023.
We look forward to seeing you at this research seminar.
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About the Talk
On June 6th, 2016, Cisco released the White paper, VNI Forecast and Methodology 2015-2020, which reported that 82 percent of Internet traffic will come from video applications such as video surveillance, content delivery networks, and 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, 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 a 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 shortly. Due to the lack of hardware computing power and limited bandwidth, lower complexity and higher compression efficiency video coding schemes are still desired. For higher video compression performance, the key optimization problems, mainly decision-making and resource allocation problems, shall be solved. In this talk, I will present the most recent research results on machine learning, and deep neural networks-based video coding. This is very different from the traditional approaches in video coding. We hope applying these intelligent techniques to video coding could allow us to go further and have more choices in trading between cost and resources.
About the Speaker
Professor KWONG Sam Tak Wu is the Chair Professor of Computational Intelligence, and concurrently as Associate Vice-President (Strategic Research) of Lingnan University. Professor Kwong is a distinguished scholar in evolutionary computation, artificial intelligence (AI) solutions, and image/video processing, with a strong record of scientific innovations and real-world impacts. Professor Kwong was listed as one of the top 2% of the world’s most cited scientists, according to the Stanford University report. He was listed as one of the top 1% of the world’s most cited scientists by Clarivate in 2022. He has also been actively engaged in knowledge transfer between academia and industry. He was elevated to IEEE Fellow in 2014 for his contributions to optimization techniques in cybernetics and video coding. He was a Fellow of the Asia-Pacific Artificial Intelligence Association (AAIA) in 2022, and the President of the IEEE Systems, Man, and Cybernetics Society (SMCS) in 2021-23. Professor Kwong has a prolific publication record with over 350 journal articles, and 160 conference papers with an h-index of 76 based on Google Scholar. He is currently the associate editor of several leading IEEE transaction journals.
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