期刊
2022 PICTURE CODING SYMPOSIUM (PCS)
卷 -, 期 -, 页码 103-107出版社
IEEE
DOI: 10.1109/PCS56426.2022.10018069
关键词
neural network; video coding; grand challenge; ISCAS
This study reviews the results and observations of the Grand Challenge on Neural Network-based Video Coding, and evaluates the performance of different neural network-based coding schemes in terms of coding efficiency and methodological innovation.
Recently years have witnessed an increasing interest in the neural network-based video coding, including end2end and hybrid schemes. To foster the research in this emerging field and provide a benchmark, we propose the Grand Challenge on Neural Network-based Video Coding (GC-NNVC) in the ISCAS 2022. In this paper, we review the grand challenge results and share interesting observations resulted from the challenge. This challenge includes two tracks, the hybrid-based and end2end-based. Different neural network-based coding schemes are evaluated according to their coding efficiency and innovations in methodologies in each track. To facilitate a solid comparison with conventional video coding techniques, the decoded sequences are evaluated in YUV 4:2:0 color format and PSNR is adopted as the distortion metric. Compared with the reference software of HEVC, submissions in hybrid track typically exhibit promising BD-rate reduction while submissions from the end2end track perform worse.
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