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DTSTAMP:20230529T195533Z
UID:82103E86-D4E4-4573-BFCB-B1AACF8C9F6A
DTSTART;TZID=America/Denver:20230526T160000
DTEND;TZID=America/Denver:20230526T180000
DESCRIPTION:Register: https://www.eventbrite.com/e/end-to-end-learned-image
 -and-video-compression-tickets-630476250437?aff=ebdssbdestsearch\n\nEvent 
 information: https://site.ieee.org/scv-cas/\n\nSpeaker: Dr. Wen-Hsiao Peng
 \n\nAbstract:\n\nThe DCT-based image and video coding technique was adopte
 d by the international\n\nstandards (ISO JPEG\, ITU H.261/264/265/266\, IS
 O MPEG-2/4/H\, and many others) for nearly\n\n30 years. Although researche
 rs are still trying to improve its efficiency by fine-tuning its\n\ncompon
 ents and parameters\, the basic structure has not changed in the past two 
 decades. The\n\narrival of deep learning recently spurred a new wave of de
 velopments in end-to-end learned\n\nimage and video compression. This fast
  growing research area has attracted more than 100+\n\npublications in the
  literature\, with the state-of-the-art end-to-end learned image compressi
 on\n\nshowing comparable compression performance to H.266/VVC intra coding
  in terms of PSNR-\n\nRGB and much better MS-SSIM results. End-to-end lear
 ned video coding is also catching up\n\nquickly. Some preliminary studies 
 report comparable PSNR-RGB results to H.265/HEVC or\n\neven H.266/VVC unde
 r the low-delay setting. These interesting results have led to intensive\n
 \nactivities in international standards organizations (e.g. JPEG AI) and v
 arious Challenges (e.g.\n\nCLIC at CVPR and Grand Challenge on Neural Netw
 ork-based Video Coding at ISCAS). In this\n\ntalk\, I shall overview (1) t
 he recent advances of this area\, (2) review some notable end-to-end\n\nle
 arned image/video compression systems\, and (3) address recent efforts in 
 creating hardware-\n\nfriendly\, low-complexity models\, and (4) look at t
 he application of end-to-end learned\n\nimage/video compression to compute
 r vision tasks\, an emerging research area also known as\n\nvisual coding 
 for machine perception. The talk will be concluded with potential research
 \n\nopportunities and an outlook for learned compression systems.\n\nHosts
 :\n\nProfessor Nam Ling\, Wilmot J. Nicholson Family Chair Professor and C
 hair\, Dept of Computer\n\nScience &amp; Engineering\, Santa Clara University\
 , USA\n\nDr. Nandish Mehta\, Chair\, IEEE Circuits and Systems Society San
 ta Clara Valley Chapter\, USA\n\nSpeaker&#39;s bio:\n\nDr. Wen-Hsiao Peng (M
 ’09-SM’13) received his Ph.D. degree from National Chiao Tung\n\nUnive
 rsity (NCTU)\, Taiwan\, in 2005. He was with the Intel Microprocessor Rese
 arch\n\nLaboratory\, USA\, from 2000 to 2001\, where he was involved in th
 e development of ISO/IEC\n\nMPEG-4 fine granularity scalability. Since 200
 3\, he has actively participated in the ISO/IEC and\n\nITU-T video coding 
 standardization process and contributed to the development of SVC\, HEVC\,
 \n\nand SCC standards. He was a Visiting Scholar with the IBM Thomas J. Wa
 tson Research Center\,\n\nUSA\, from 2015 to 2016. He is currently a Profe
 ssor with the Computer Science Department\,\n\nNational Yang Ming Chiao Tu
 ng University\, Taiwan. He has authored over 75+\n\njournal/conference pap
 ers and over 60 ISO/IEC and ITU-T standards contributions. His research\n\
 ninterests include learning-based video/image compression\, deep/machine l
 earning\, multimedia\n\nanalytics\, and computer vision. Dr. Peng was Chai
 r of the IEEE Circuits and Systems Society\n\n(CASS) Visual Signal Process
 ing (VSPC) Technical Committee from 2020-2022. He was\n\nTechnical Program
  Co-chair for 2021 IEEE VCIP\, 2011 IEEE VCIP\, 2017 IEEE ISPACS\, and\n\n
 2018 APSIPA ASC\; Publication Chair for 2019 IEEE ICIP\; Area Chair/Sessio
 n Chair/Tutorial\n\nSpeaker/Special Session Organizer for IEEE ICME\, IEEE
  VCIP\, and APSIPA ASC\; and\n\nTrack/Session Chair and Review Committee M
 ember for IEEE ISCAS. He served as AEiC for\n\nDigital Communications for 
 IEEE JETCAS and Associate Editor for IEEE TCSVT. He was\n\nLead Guest Edit
 or\, Guest Editor and SEB Member for IEEE JETCAS\, and Guest Editor for\n\
 nIEEE TCAS-II. He was Distinguished Lecturer of APSIPA and the IEEE CASS.\
 n\nThis event can be attended in-person or via the following zoom link:\n\
 nJoin Zoom Meeting\n\nhttps://scu.zoom.us/j/98160784214?pwd=TXJZeE9ERGt1RW
 9lRnhPSGZrTFQwdz09\n\nMeeting ID: 981 6078 4214\n\nPassword: 193987\n\nJoi
 n by phone:\n\n+1 (669) 900-6833\n\nMeeting ID: 981 6078 4214\n\nOne tap m
 obile\n\n+16699006833\,\,98160784214#\n\nRoom: 1301\, Bldg: Sobrato Campus
  for Discovery and Innovation\, SCU\, Santa Clara\, CA 95053\, Santa Clara
 \, California\, United States\, 95053
LOCATION:Room: 1301\, Bldg: Sobrato Campus for Discovery and Innovation\, S
 CU\, Santa Clara\, CA 95053\, Santa Clara\, California\, United States\, 9
 5053
ORGANIZER:nandish@ieee.org
SEQUENCE:9
SUMMARY:End-to-End Learned Image and Video Compression
URL;VALUE=URI:https://events.vtools.ieee.org/m/360756
X-ALT-DESC:Description: &lt;br /&gt;&lt;p&gt;Register: https://www.eventbrite.com/e/end
 -to-end-learned-image-and-video-compression-tickets-630476250437?aff=ebdss
 bdestsearch&lt;/p&gt;\n&lt;p&gt;Event information: https://site.ieee.org/scv-cas/&lt;/p&gt;\
 n&lt;div class=&quot;eds-l-mar-vert-6 eds-l-sm-mar-vert-4 eds-text-bm structured-c
 ontent-rich-text&quot;&gt;\n&lt;div class=&quot;eds-text--left&quot;&gt;\n&lt;p&gt;&lt;strong&gt;Speaker: Dr. 
 Wen-Hsiao Peng&lt;/strong&gt;&lt;/p&gt;\n&lt;p&gt;&lt;strong&gt;Abstract:&lt;/strong&gt;&lt;/p&gt;\n&lt;p&gt;The DCT
 -based image and video coding technique was adopted by the international&lt;/
 p&gt;\n&lt;p&gt;standards (ISO JPEG\, ITU H.261/264/265/266\, ISO MPEG-2/4/H\, and 
 many others) for nearly&lt;/p&gt;\n&lt;p&gt;30 years. Although researchers are still t
 rying to improve its efficiency by fine-tuning its&lt;/p&gt;\n&lt;p&gt;components and 
 parameters\, the basic structure has not changed in the past two decades. 
 The&lt;/p&gt;\n&lt;p&gt;arrival of deep learning recently spurred a new wave of develo
 pments in end-to-end learned&lt;/p&gt;\n&lt;p&gt;image and video compression. This fas
 t growing research area has attracted more than 100+&lt;/p&gt;\n&lt;p&gt;publications 
 in the literature\, with the state-of-the-art end-to-end learned image com
 pression&lt;/p&gt;\n&lt;p&gt;showing comparable compression performance to H.266/VVC i
 ntra coding in terms of PSNR-&lt;/p&gt;\n&lt;p&gt;RGB and much better MS-SSIM results.
  End-to-end learned video coding is also catching up&lt;/p&gt;\n&lt;p&gt;quickly. Some
  preliminary studies report comparable PSNR-RGB results to H.265/HEVC or&lt;/
 p&gt;\n&lt;p&gt;even H.266/VVC under the low-delay setting. These interesting resul
 ts have led to intensive&lt;/p&gt;\n&lt;p&gt;activities in international standards org
 anizations (e.g. JPEG AI) and various Challenges (e.g.&lt;/p&gt;\n&lt;p&gt;CLIC at CVP
 R and Grand Challenge on Neural Network-based Video Coding at ISCAS). &amp;nbs
 p\;In this&lt;/p&gt;\n&lt;p&gt;talk\, I shall overview (1) the recent advances of this
  area\, (2) review some notable end-to-end&lt;/p&gt;\n&lt;p&gt;learned image/video com
 pression systems\, and (3) address recent efforts in creating hardware-&lt;/p
 &gt;\n&lt;p&gt;friendly\, low-complexity models\, and (4) look at the application o
 f end-to-end learned&lt;/p&gt;\n&lt;p&gt;image/video compression to computer vision ta
 sks\, an emerging research area also known as&lt;/p&gt;\n&lt;p&gt;visual coding for ma
 chine perception. The talk will be concluded with potential research&lt;/p&gt;\n
 &lt;p&gt;opportunities and an outlook for learned compression systems.&lt;/p&gt;\n&lt;p&gt;&amp;
 nbsp\;&lt;/p&gt;\n&lt;p&gt;&lt;strong&gt;Hosts:&lt;/strong&gt;&lt;/p&gt;\n&lt;p&gt;&lt;strong&gt;Professor Nam Ling&lt;
 /strong&gt;\, Wilmot J. Nicholson Family Chair Professor and Chair\, Dept of 
 Computer&lt;/p&gt;\n&lt;p&gt;Science &amp;amp\; Engineering\, Santa Clara University\, USA
 &lt;/p&gt;\n&lt;p&gt;&lt;strong&gt;Dr. Nandish Mehta&lt;/strong&gt;\, Chair\, IEEE Circuits and Sy
 stems Society Santa Clara Valley Chapter\, USA&lt;/p&gt;\n&lt;p&gt;&amp;nbsp\;&lt;/p&gt;\n&lt;p&gt;&lt;st
 rong&gt;Speaker&#39;s bio:&lt;/strong&gt;&lt;/p&gt;\n&lt;p&gt;Dr. Wen-Hsiao Peng (M&amp;rsquo\;09-SM&amp;rs
 quo\;13) received his Ph.D. degree from National Chiao Tung&lt;/p&gt;\n&lt;p&gt;Univer
 sity (NCTU)\, Taiwan\, in 2005. He was with the Intel Microprocessor Resea
 rch&lt;/p&gt;\n&lt;p&gt;Laboratory\, USA\, from 2000 to 2001\, where he was involved i
 n the development of ISO/IEC&lt;/p&gt;\n&lt;p&gt;MPEG-4 fine granularity scalability. 
 Since 2003\, he has actively participated in the ISO/IEC and&lt;/p&gt;\n&lt;p&gt;ITU-T
  video coding standardization process and contributed to the development o
 f SVC\, HEVC\,&lt;/p&gt;\n&lt;p&gt;and SCC standards. He was a Visiting Scholar with t
 he IBM Thomas J. Watson Research Center\,&lt;/p&gt;\n&lt;p&gt;USA\, from 2015 to 2016.
  He is currently a Professor with the Computer Science Department\,&lt;/p&gt;\n&lt;
 p&gt;National Yang Ming Chiao Tung University\, Taiwan. He has authored over 
 75+&lt;/p&gt;\n&lt;p&gt;journal/conference papers and over 60 ISO/IEC and ITU-T standa
 rds contributions. His research&lt;/p&gt;\n&lt;p&gt;interests include learning-based v
 ideo/image compression\, deep/machine learning\, multimedia&lt;/p&gt;\n&lt;p&gt;analyt
 ics\, and computer vision. Dr. Peng was Chair of the IEEE Circuits and Sys
 tems Society&lt;/p&gt;\n&lt;p&gt;(CASS) Visual Signal Processing (VSPC) Technical Comm
 ittee from 2020-2022. He was&lt;/p&gt;\n&lt;p&gt;Technical Program Co-chair for 2021 I
 EEE VCIP\, 2011 IEEE VCIP\, 2017 IEEE ISPACS\, and&lt;/p&gt;\n&lt;p&gt;2018 APSIPA ASC
 \; Publication Chair for 2019 IEEE ICIP\; Area Chair/Session Chair/Tutoria
 l&lt;/p&gt;\n&lt;p&gt;Speaker/Special Session Organizer for IEEE ICME\, IEEE VCIP\, an
 d APSIPA ASC\; and&lt;/p&gt;\n&lt;p&gt;Track/Session Chair and Review Committee Member
  for IEEE ISCAS. He served as AEiC for&lt;/p&gt;\n&lt;p&gt;Digital Communications for 
 IEEE JETCAS and Associate Editor for IEEE TCSVT. He was&lt;/p&gt;\n&lt;p&gt;Lead Guest
  Editor\, Guest Editor and SEB Member for IEEE JETCAS\, and Guest Editor f
 or&lt;/p&gt;\n&lt;p&gt;IEEE TCAS-II. He was Distinguished Lecturer of APSIPA and the I
 EEE CASS.&lt;/p&gt;\n&lt;/div&gt;\n&lt;/div&gt;\n&lt;div class=&quot;eds-l-mar-vert-6 eds-l-sm-mar-v
 ert-4 eds-text-bm structured-content-rich-text&quot;&gt;\n&lt;div class=&quot;eds-text--le
 ft&quot;&gt;\n&lt;p&gt;&lt;strong&gt;This event can be attended in-person or via the following
  zoom link:&lt;/strong&gt;&lt;/p&gt;\n&lt;p&gt;&amp;nbsp\;&lt;/p&gt;\n&lt;p&gt;Join Zoom Meeting&lt;/p&gt;\n&lt;p&gt;htt
 ps://scu.zoom.us/j/98160784214?pwd=TXJZeE9ERGt1RW9lRnhPSGZrTFQwdz09&lt;/p&gt;\n&lt;
 p&gt;Meeting ID: 981 6078 4214&lt;/p&gt;\n&lt;p&gt;Password: 193987&lt;/p&gt;\n&lt;p&gt;Join by phone
 :&lt;/p&gt;\n&lt;p&gt;+1 (669) 900-6833&lt;/p&gt;\n&lt;p&gt;Meeting ID: 981 6078 4214&lt;/p&gt;\n&lt;p&gt;One 
 tap mobile&lt;/p&gt;\n&lt;p&gt;+16699006833\,\,98160784214#&lt;/p&gt;\n&lt;/div&gt;\n&lt;/div&gt;\n&lt;p&gt;&amp;n
 bsp\;&lt;/p&gt;
END:VEVENT
END:VCALENDAR