Technical Program

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SS-01: Neural Network-based Visual Data Compression

Paper Presentations and Interactive Q&A Time: Monday, 26 October, 09:00 - 13:25
Virtual Session: View on Virtual Platform
Session Chairs: Mounir Kaaniche, Université Sorbonne Paris Nord and Amel Benazza-Benyahia, COSIM Lab., SUP'COM, University of Carthage
 
 SS-01.1: A STUDY OF PREDICTION METHODS BASED ON MACHINE LEARNING TECHNIQUES FOR LOSSLESS IMAGE CODING
         Ionut Schiopu; Vrije Universiteit Brussel
         Adrian Munteanu; Vrije Universiteit Brussel
 
 SS-01.2: OPTIMIZED LIFTING SCHEME BASED ON A DYNAMICAL FULLY CONNECTED NETWORK FOR IMAGE CODING
         Tassnim Dardouri; Université Sorbonne Paris Nord
         Mounir Kaaniche; Université Sorbonne Paris Nord
         Amel Benazza-Benyahia; University of Carthage, SUP’COM
         Jean-Christophe Pesquet; Université Paris Saclay, CentraleSupelec
 
 SS-01.3: OPTIMIZED CONVOLUTIONAL NEURAL NETWORKS FOR VIDEO INTRA PREDICTION
         Maria Meyer; RWTH Aachen University
         Jonathan Wiesner; RWTH Aachen University
         Christian Rohlfing; RWTH Aachen University
 
 SS-01.4: CHANNEL-WISE AUTOREGRESSIVE ENTROPY MODELS FOR LEARNED IMAGE COMPRESSION
         David Minnen; Google
         Saurabh Singh; Google
 
 SS-01.5: HYBRID LEARNING-BASED AND HEVC-BASED CODING OF LIGHT FIELDS
         Milan Stepanov; Universite Paris-Saclay, CNRS, CentraleSupelec, Laboratoire des signaux et systemes
         Giuseppe Valenzise; Universite Paris-Saclay, CNRS, CentraleSupelec, Laboratoire des signaux et systemes
         Frederic Dufaux; Universite Paris-Saclay, CNRS, CentraleSupelec, Laboratoire des signaux et systemes
 
 SS-01.6: END-TO-END LEARNING OF COMPRESSIBLE FEATURES
         Saurabh Singh; Google
         Sami Abu-El-Haija; University of Southern California
         Nick Johnston; Google
         Johannes Ballé; Google
         Abhinav Shrivastava; University of Maryland, College Park
         George Toderici; Google
 
 SS-01.7: POINT CLOUD GEOMETRY SCALABLE CODING WITH A SINGLE END-TO-END DEEP LEARNING MODEL
         André F. R. Guarda; Instituto Superior Técnico – Universidade de Lisboa and Instituto de Telecomunicações
         Nuno M. Rodrigues; ESTG – Instituto Politécnico de Leiria and Instituto de Telecomunicações
         Fernando Pereira; Instituto Superior Técnico – Universidade de Lisboa and Instituto de Telecomunicações
 
 SS-01.8: END-TO-END LEARNED IMAGE COMPRESSION WITH FIXED POINT WEIGHT QUANTIZATION
         Heming Sun; Waseda University
         Zhengxue Cheng; Waseda University
         Masaru Takeuchi; Waseda University
         Jiro Katto; Waseda University
 
 SS-01.9: EFFICIENT FIXED-POINT IMPLEMENTATION OF MATRIX-BASED INTRA PREDICTION
         Michael Schäfer; Fraunhofer Heinrich Hertz Institute (HHI)
         Björn Stallenberger; Fraunhofer Heinrich Hertz Institute (HHI)
         Jonathan Pfaff; Fraunhofer Heinrich Hertz Institute (HHI)
         Philipp Helle; Fraunhofer Heinrich Hertz Institute (HHI)
         Heiko Schwarz; Fraunhofer Heinrich Hertz Institute (HHI)
         Detlev Marpe; Fraunhofer Heinrich Hertz Institute (HHI)
         Thomas Wiegand; Fraunhofer Heinrich Hertz Institute (HHI)
 
 SS-01.10: SCALABLE LEARNED IMAGE COMPRESSION WITH A RECURRENT NEURAL NETWORKS-BASED HYPERPRIOR
         Rige Su; Waseda University
         Zhengxue Cheng; Waseda University
         Heming Sun; Waseda University
         Jiro Katto; Waseda University
 
 SS-01.11: MACHINE LEARNING BASED SYMBOL PROBABILITY DISTRIBUTION PREDICTION FOR ENTROPY CODING IN AV1
         Mingliang Chen; University of Maryland, College Park
         Hui Su; Google
         Sai Deng; Google
         Yaowu Xu; Google
 
 SS-01.12: GUIDED CNN RESTORATION WITH EXPLICITLY SIGNALED LINEAR COMBINATION
         Lingyi Kong; Hangzhou Normal University
         Dandan Ding; Hangzhou Normal University
         Fuchang Liu; Hangzhou Normal University
         Debargha Mukherjee; Google
         Urvang Joshi; Google
         Yue Chen; Google