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End-to-End Learned Image and Video Compression

May 26, 2023 @ 4:00 pm - 6:00 pm MDT

Register: https://www.eventbrite.com/e/end-to-end-learned-image-and-video-compression-tickets-630476250437?aff=ebdssbdestsearch
Event information: https://site.ieee.org/scv-cas/
Speaker: Dr. Wen-Hsiao Peng
Abstract:
The DCT-based image and video coding technique was adopted by the international
standards (ISO JPEG, ITU H.261/264/265/266, ISO MPEG-2/4/H, and many others) for nearly
30 years. Although researchers are still trying to improve its efficiency by fine-tuning its
components and parameters, the basic structure has not changed in the past two decades. The
arrival of deep learning recently spurred a new wave of developments in end-to-end learned
image and video compression. This fast growing research area has attracted more than 100+
publications in the literature, with the state-of-the-art end-to-end learned image compression
showing comparable compression performance to H.266/VVC intra coding in terms of PSNR-
RGB and much better MS-SSIM results. End-to-end learned video coding is also catching up
quickly. Some preliminary studies report comparable PSNR-RGB results to H.265/HEVC or
even H.266/VVC under the low-delay setting. These interesting results have led to intensive
activities in international standards organizations (e.g. JPEG AI) and various Challenges (e.g.
CLIC at CVPR and Grand Challenge on Neural Network-based Video Coding at ISCAS). In this
talk, I shall overview (1) the recent advances of this area, (2) review some notable end-to-end
learned image/video compression systems, and (3) address recent efforts in creating hardware-
friendly, low-complexity models, and (4) look at the application of end-to-end learned
image/video compression to computer vision tasks, an emerging research area also known as
visual coding for machine perception. The talk will be concluded with potential research
opportunities and an outlook for learned compression systems.
Hosts:
Professor Nam Ling, Wilmot J. Nicholson Family Chair Professor and Chair, Dept of Computer
Science & Engineering, Santa Clara University, USA
Dr. Nandish Mehta, Chair, IEEE Circuits and Systems Society Santa Clara Valley Chapter, USA
Speaker's bio:
Dr. Wen-Hsiao Peng (M’09-SM’13) received his Ph.D. degree from National Chiao Tung
University (NCTU), Taiwan, in 2005. He was with the Intel Microprocessor Research
Laboratory, USA, from 2000 to 2001, where he was involved in the development of ISO/IEC
MPEG-4 fine granularity scalability. Since 2003, he has actively participated in the ISO/IEC and
ITU-T video coding standardization process and contributed to the development of SVC, HEVC,
and SCC standards. He was a Visiting Scholar with the IBM Thomas J. Watson Research Center,
USA, from 2015 to 2016. He is currently a Professor with the Computer Science Department,
National Yang Ming Chiao Tung University, Taiwan. He has authored over 75+
journal/conference papers and over 60 ISO/IEC and ITU-T standards contributions. His research
interests include learning-based video/image compression, deep/machine learning, multimedia
analytics, and computer vision. Dr. Peng was Chair of the IEEE Circuits and Systems Society
(CASS) Visual Signal Processing (VSPC) Technical Committee from 2020-2022. He was
Technical Program Co-chair for 2021 IEEE VCIP, 2011 IEEE VCIP, 2017 IEEE ISPACS, and
2018 APSIPA ASC; Publication Chair for 2019 IEEE ICIP; Area Chair/Session Chair/Tutorial
Speaker/Special Session Organizer for IEEE ICME, IEEE VCIP, and APSIPA ASC; and
Track/Session Chair and Review Committee Member for IEEE ISCAS. He served as AEiC for
Digital Communications for IEEE JETCAS and Associate Editor for IEEE TCSVT. He was
Lead Guest Editor, Guest Editor and SEB Member for IEEE JETCAS, and Guest Editor for
IEEE TCAS-II. He was Distinguished Lecturer of APSIPA and the IEEE CASS.
This event can be attended in-person or via the following zoom link:
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https://scu.zoom.us/j/98160784214?pwd=TXJZeE9ERGt1RW9lRnhPSGZrTFQwdz09
Meeting ID: 981 6078 4214
Password: 193987
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Meeting ID: 981 6078 4214
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Room: 1301, Bldg: Sobrato Campus for Discovery and Innovation, SCU, Santa Clara, CA 95053, Santa Clara, California, United States, 95053

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