2021 IEEE International Conference on Image Processing
19-22 September 2021 • Anchorage, Alaska, USA
Imaging Without Borders
| Monday, September 20, 11:30 - 12:30, Alaska local time (AKDT/UTC−08:00) |
Stories from the technology crucible at Google, more specifically YouTube, Chrome, and Stadia.
Since 2012, the Chrome Media team and YouTube Media Algorithms team have been showcasing their research and development activities at ICIP. Previous workshops have highlighted work in new video codecs and novel video enhancement pipelines employed in YouTube. The goal is to motivate young engineers and researchers in the area of audiovisual processing and help show how research ideas become products in a video system. Engineers working in product teams speak about their experiences and highlight new technology being deployed to huge audiences worldwide.
This year we have Stadia team joining us. Also, Tencent will be co-presenting one of the talks. We’ll talk about the latest in video codecs and quality assessment for UGC and Gaming. The workshop is made up of 3 talks:
Debargha Mukherjee received his M.S./Ph.D. degrees in ECE from University of California Santa Barbara in 1999. Through 2009 he was with Hewlett Packard Laboratories, conducting research on video/image coding and processing. Since 2010 he has been with Google LLC, where he is currently a Principal Engineer/Director leading next generation video codec research and development efforts. Debargha has authored/co-authored more than 100 papers on various signal processing topics, and holds more than 100 US patents, with many more pending. He has delivered many workshops and talks on Google's royalty-free line of codecs since 2012, and more recently on the AV1 video codec from the Alliance for Open Media (AOM). He currently serves as a Senior Area Editor of the IEEE Trans. on Image Processing, and as a member of the IEEE Image, Video, and Multidimensional Signal Processing Technical Committee (IVMSP TC).
Onur G. Guleryuz is a Software Engineer at Google working on machine learning, computer vision, and compression problems. Prior to Google he worked at LG Electronics, Futurewei, NTT DoCoMo, and Seiko-Epson all in Silicon Valley. Before coming to Silicon Valley in 2000 he served as an Asst. Prof. with NYU Tandon School of Engineering in New York.
His research interests include topics in statistical signal processing, computer vision, and information theory. He has served in numerous panels, conference committees, and media-related industry standardization bodies. He has authored an extensive number of refereed papers, granted US patents, and has leading edge contributions to products ranging from mobile phones to displays and printers. He has been an active member of IEEE Signal Processing Society, currently serving as Chair of the IEEE Signal Processing Society Image, Video, and Multidimensional Signal Processing Technical Committee.
He received the BS degrees in electrical engineering and physics from Bogazici University, Istanbul, Turkey in 1991, the M.S. degree in engineering and applied science from Yale University, New Haven, CT in 1992, and the Ph.D. degree in electrical engineering from University of Illinois at Urbana-Champaign (UIUC), Urbana, in 1997. He received the National Science Foundation Career Award, the IEEE Signal Processing Society Best Paper Award, the IEEE International Conference on Image Processing Best Paper Award, the Seiko-Epson Corporation President's Award for Research and Development, and the DoCoMo Communications Laboratories President's Award for Research.
Madhu P Krishnan received the M.S degree in Electrical Engineering from the University of Texas at Arlington. He joined Tencent (Palo Alto, CA, USA) in 2019 as a Senior Research Engineer in Tencent Media Lab, where he has been actively contributing to the development of next-generation video coding standard. Before that he was a Research Engineer with FastVDO LLC, Melbourne, FL, USA. He has been actively contributing technical proposals for international video coding standard such as HEVC and next-generation video coding beyond AV1. He has published over 10 conference papers in the past 10 years. His main research interests are in the area of image and video coding, computer vision and pattern recognition.
Dr. Balu Adsumilli is currently the Head of Media Algorithms group at YouTube/Google. Prior to YouTube, he was Sr. Manager Advanced Technology at GoPro, and before that, he was Sr. Research Scientist at Citrix Online, at both places developing algorithms for images/video quality enhancement, compression, capture, and streaming. He received his masters at the University of Wisconsin Madison, and his PhD at the University of California Santa Barbara. He has co-authored more than 70 papers and 80 granted patents with many more pending. He serves on the Television Academy and NATAS technology committees, on the IEEE MMSP Technical Committee, and on ACM MHV Steering Committee. He is an organizing committee member for various conferences and held numerous workshops. He is an active member of IEEE, ACM, SPIE, and Visual Effects Society. His fields of research include image/video processing, audio and video quality, video compression and transcoding, spherical capture/AR/VR, visual effects, and related areas.
Yilin Wang is a member of the Media Algorithms team at YouTube/Google. He spent the last seven years on improving YouTube video processing and transcoding infrastructures, and building video quality metrics. Beside the video engineering work, he is also an active researcher in video quality related areas, and published papers in CVPR, ICCV, TIP, ICIP, etc. He received his PhD from the University of North Carolina at Chapel Hill in 2014, working on topics in computer vision and image processing.
Ramachandra Tahasildar (Ram) is currently the head of Audio/Video engineering at Google Stadia. He has been at Google since 2005 and played key roles in various products like Piper, YouTube TV, Project Stream and Stadia. For the last 8 years he has been focusing primarily on low latency video processing in the cloud