George Toderici received his Ph.D. in Computer Science from the University of Houston in 2008 where his research focused on 2D-to-3D face recognition, and joined Google in 2008. His current work at Google Research is focused on lossy multimedia compression using neural networks. His past projects at Google include the design of neural-network architectures and classical approaches for video classification, action recognition, YouTube channel recommendations, and video enhancement. He has helped organize the THUMOS-2014 and YouTube-8M (CVPR 2017, ECCV 2018, ICCV 2019) video classification challenges, and contributed to the design of the Sports-1M dataset. George is one of the original CLIC organizers and has helped organize this workshop at CVPR 2018-2021. He has also served as Area Chair for the ACM Multimedia Conference in 2014, and is a regular reviewer for CVPR, ICCV, and NeurIPS. (read more)

Radu Timofte is research group leader in the Computer Vision Laboratory, at ETH Zurich, Switzerland. He obtained a PhD degree in Electrical Engineering at the KU Leuven, Belgium in 2014, the MSc at the Univ. of Eastern Finland in 2007, and the Dipl. Eng. at the Technical Univ. of Iasi, Romania in 2006. He serves a reviewer for top journals (such as TPAMI, TIP, IJCV, TNNLS, TCSVT, CVIU, PR) and conferences (ICCV, CVPR, ECCV, NeurIPS), as area chair for ACCV 2019 and as area editor for Elsevier’s CVIU journal. His work received a best scientific paper award at ICPR 2012, the best paper award at CVVT workshop (ECCV 2012), the best paper award at ChaLearn LAP workshop (ICCV 2015), the best scientific poster award at EOS 2017, the honorable mention award at FG 2017, and his team won a number of challenges including traffic sign detection (IJCNN 2013) and apparent age estimation (ICCV 2015). He is co-founder of Merantix and organizer of NTIRE ‘16, ’17 and ’18 events. His current research interests include sparse and collaborative representations, deep learning, optical flow, compression, image restoration and enhancement. (read more)

Johannes Ballé is a Research Scientist at Google. His current work focuses on data compression, rate–distortion optimization and models of visual perception. He defended his master's and doctoral theses on signal processing and image compression at RWTH Aachen University in 2008 and 2012, respectively, working with J.-R. Ohm. This was followed by a brief collaboration with J. Portilla at CSIC in Madrid, Spain, and a postdoctoral fellowship at NYU’s Center for Neural Science with E.P. Simoncelli. There, he studied the relationship between perception and image statistics, and pioneered the use of stochastic rate–distortion optimization and deep learning for end-to-end optimized image compression. He joined Google in early 2017 to continue working in this line of research. Johannes has served as a reviewer for top-tier publications in both machine learning and image processing, such as NeurIPS, ICLR, ICML, Picture Coding Symposium and several IEEE Transactions, and has been a co-organizer of the Workshop and Challenge on Learned Image Compression (CLIC) since 2018. (read more)

Eirikur Agustsson is a Senior Research Scientist at Google Research in Zurich, focusing on learned compression. He has mainly worked on image and video compression using neural networks, almost lossless analog compression, image-super-resolution and generative adversarial networks. He has reviewed for many of the top computer vision and machine learning conferences and journals (such as NeurIPS, ICML, ICLR, CVPR and IEEE Transactions on Pattern Analysis and Machine Intelligence) and co-organized numerous workshops (the WebVision challenge & workshop at CVPR '18 and '18, the NTIRE challenge & workshop at CVPR '17 and the CLIC Workshop & Challenge on Learned Image Compression since CVPR '18). (read more)

Nick Johnston works as a Software Engineer within the Machine Intelligence group at Google. He has previously published image compression work at CVPR. His research interests are in leveraging the power of deep learning and computer vision for improved rate-distortion performance in image compression. Additionally, he is interested in neural network optimization for mobile devices and embedded systems. Nick received his BSc in Computer Engineering from Iowa State University. (read more)

Fabian Mentzer works as a Research Scientist at Google, focusing on neural video compression. Before that, he obtained his PhD at the Computer Vision Lab of ETH Zurich, under the supervision of Prof. Luc Van Gool. He focussed on learned lossy and lossless image compression using deep neural networks and investigating the feature representations learned by such networks, with works published at NIPS, ICLR, CVPR and ICCV. . (read more)

Zeina Sinno is a software engineer at Apple. Her interests include machine learning with applications to video processing and multimedia. She received her M.S. and Ph.D. degrees in electrical and computer engineering from The University of Texas at Austin in 2016 and 2019, respectively. She is a reviewer for the IEEE Transactions on Image Processing, IEEE Transactions on Circuits Systems and Video Technology, IEEE Transactions on Multimedia, IEEE Access, PCS, and ICIP. (read more)

Andrey Norkin received the M.Sc. degree in computer engineering from Ural State Technical University, Yekaterinburg, Russia, in 2002 and the Doctor of Science degree in signal processing from Tampere University of Technology, Tampere, Finland, in 2007. From 2009 to 2015, he was with Ericsson, Sweden, conducting research on video compression and 3D video. In 2014, he worked on video encoding techniques for TV broadcasting products at Ericsson TV, Southampton, UK. Since 2015, Dr. Norkin has been with Netflix, USA as a Senior Research Scientist working on encoding techniques for OTT video streaming, High Dynamic Range (HDR) video, and new video compression algorithms. He has participated in ITU-T and MPEG efforts on developing video compression standards, having multiple technical contributions and coordinating work in certain areas of the codec development. Andrey has been actively contributing to the Alliance for Open Media (AOM) development of the AV1 video codec where he is currently a co-chair of the Codec Working Group. Dr. Norkin's research interests include video compression, OTT streaming, HDR video, and machine learning techniques. (read more)

Krishna Rapaka currently works as a Software Engineer at Apple. In the last two decades, his research interests focussed in the areas of multimedia compression/processing using hybrid and neural network architectures, design of embedded codecs for mobile and streaming applications. He was involved in the standardization of scalable and screen content extensions of HEVC codec and architected hardware codecs in Qualcomm and Texas Instruments. He is a reviewer for TCSVT, ICIP, CVPR, SPIE and co-chaired for activities in JCT-VC/MPEG and AOM. Krishna received his MaSc in electrical engineering from the University of Waterloo, Canada. (read more)

Erfan Noury is a ML Research Engineer in the Camera team at Apple, and a Master’s student in the Computer Vision lab. at University of Maryland, Baltimore County, under the supervision of Prof. Hamed Pirsiavash. His research interests include self-supervised representation learning in computer vision, and image compression.

Ross Cutler is a Partner Applied Scientist Manager at Microsoft in the IC4 group where he manages the IC3-AI team of applied scientists and software engineers with the focus of improving Teams/Skype audio/video quality and reliability and enabling new functionality with AI. He has been with Microsoft since 2001, starting as a researcher in Microsoft Research. He has published 50+ conference papers, journal papers, and book chapters, and has 95+ granted patents in the areas of computer vision, audio processing, machine learning, optics, and acoustics. Ross received his Ph.D. in Computer Science (2000) in the area of computer vision from the University of Maryland, College Park. He is a reviewer in CVPR, ICCV, ICML, ICASSP, and INTERSPEECH. (read more)

Luca Versari obtained a Ph.D. degree with the University of Pisa, Italy. He is a Core Member of the JPEG XL Development Team. He studied algorithms for pattern matching, graphs, hashing, and data compression with the University of Pisa. He is currently a Senior Software Engineer with Google Research. He is responsible for the algorithms and technical architecture of the image quality related aspects of JPEG XL, including integral transforms, color spaces, intra/inter-frame copying, progressive decoding, animation, context modeling, tiling, entropy coding, codec optimization, and integration of psychovisual modeling. (read more)

Fabien Racapé is a senior researcher at interdigital AI Lab, focussing on video compression using hybrid and neural network based methods. He has been involved in H.266/VVC and MPEG Neural Network compressed Representations (NNR) standardization activities. Fabien received his M.Sc. in signal processing and telecommunications from the Grenoble Institute of Technology, France, in 2008, and his PhD degree from the National Institute of Applied Science (INSA), Rennes, France, in 2011. (read more)