HP-V2.V11.8
DEEP INCREMENTAL OPTICAL FLOW CODING FOR LEARNED VIDEO COMPRESSION
Chih-Peng Chang, Peng-Yu Chen, Yung-Han Ho, Wen-Hsiao Peng, National Yang Ming Chiao Tung University, Taiwan
Session:
Machine Learning for Image and Video Communications
Track:
Applications of Machine Learning
Location:
Gather.Town 11
Presentation Time:
Thu, 6 Oct, 22:00 - 23:00 China Standard Time (UTC +8)
Thu, 6 Oct, 16:00 - 17:00 Central European Time (UTC +1)
Thu, 6 Oct, 14:00 - 15:00 UTC
Thu, 6 Oct, 10:00 - 11:00 Eastern Time (UTC -4)
Thu, 6 Oct, 16:00 - 17:00 Central European Time (UTC +1)
Thu, 6 Oct, 14:00 - 15:00 UTC
Thu, 6 Oct, 10:00 - 11:00 Eastern Time (UTC -4)
Session Co-Chairs:
Jean-Christophe Pesquet, CentraleSupélec and Andrea Cavallaro, Queen Mary University of London and Rebecca Willett, University of Chicago
Presentation
Discussion
Resources
No resources available.
Session HP-V2.V11
HP-V2.V11.1: A NEURAL NETWORK LIFTING BASED SECONDARY TRANSFORM FOR IMPROVED FULLY SCALABLE IMAGE COMPRESSION IN JPEG 2000
Xinyue Li, Aous Naman, David Taubman, University of New South Wales, Australia, Australia
HP-V2.V11.2: INTRA-INTER PREDICTION FOR VERSATILE VIDEO CODING USING A RESIDUAL CONVOLUTIONAL NEURAL NETWORK
Philipp Merkle, Martin Winken, Jonathan Pfaff, Heiko Schwarz, Detlev Marpe, Thomas Wiegand, Fraunhofer Heinrich Hertz Institute (HHI), Germany
HP-V2.V11.3: PANORAMIC VIEWPORT PREDICTION RELYING ON EMOTIONAL ATTENTION MAP
Yuxiao Xu, Yongkai Huo, Yukai Song, Shenzhen University, China
HP-V2.V11.4: ADAPTIVE LOOP FILTER WITH A CNN-BASED CLASSIFICATION
Wang-Q Lim, Jonathan Pfaff, Björn Stallenberger, Johannes Erfurt, Heiko Schwarz, Detlev Marpe, Thomas Wiegand, Fraunhofer Heinrich Hertz Institute (HHI), Germany
HP-V2.V11.5: THE EFFECT OF SPATIAL AND TEMPORAL OCCLUSION ON WORD LEVEL SIGN LANGUAGE RECOGNITION
Ajkel Mino, Mirela Popa, Alexia Briassouli, Maastricht University, Netherlands
HP-V2.V11.6: GM-RF: AN AV1 INTRA-FRAME FAST DECISION BASED ON RANDOM FOREST
Pablo Rosa, Daniel Palomino, Marcelo Porto, Luciano Agostini, Federal University of Pelotas, Brazil
HP-V2.V11.7: LOW-COMPLEXITY MULTI-TYPE TREE PARTITIONING FOR VERSATILE VIDEO CODING BASED ON MACHINE LEARNING
Matheus Lindino, Bruno Zatt, Guilherme Correa, Universidade Federal de Pelotas (UFPel), Brazil; Mateus Grellert, Universidade Federal de Santa Catarina (UFSC), Brazil
HP-V2.V11.8: DEEP INCREMENTAL OPTICAL FLOW CODING FOR LEARNED VIDEO COMPRESSION
Chih-Peng Chang, Peng-Yu Chen, Yung-Han Ho, Wen-Hsiao Peng, National Yang Ming Chiao Tung University, Taiwan
HP-V2.V11.10: LEARNED IMAGE COMPRESSION WITH MULTI-SCALE SPATIAL AND CONTEXTUAL INFORMATION FUSION
Ziyi Liu, Hanli Wang, Taiyi Su, Tongji University, China
HP-V2.V11.11: LOCAL AND GLOBAL FUSION NETWORK FOR LEARNED IMAGE COMPRESSION
Gai Zhang, Xinfeng Zhang, University of Chinese Academy of Sciences, China, China; Shuyuan Zhu, University of Electronic Science and Technology, China, China
HP-V2.V11.12: LEARNING-BASED END-TO-END VIDEO COMPRESSION WITH SPATIAL-TEMPORAL ADAPTATION
Zhaobin Zhang, Yue Li, Kai Zhang, Li Zhang, Yuwen He, Bytedance Inc, United States of America
HP-V2.V11.13: Transform Skip Inspired End-to-End Compression for Screen Content Image
Meng Wang, Shiqi Wang, City University of Hong Kong, Hong Kong; Kai Zhang, Li Zhang, Yaojun Wu, Yue Li, Junru Li, Bytedance Inc., United States of America