Content-Adaptive Motion Rate Adaption For Learned Video Compression

Chih Hsuan Lin, Yi Hsin Chen, Wen-Hsiao Peng

研究成果: Paper同行評審


This paper introduces an online motion rate adaptation scheme for learned video compression, with the aim of
achieving content-adaptive coding on individual test sequences
to mitigate the domain gap between training and test data. It
features a patch-level bit allocation map, termed the α-map, to
trade off between the bit rates for motion and inter-frame coding
in a spatially-adaptive manner. We optimize the α-map through
an online back-propagation scheme at inference time. Moreover,
we incorporate a look-ahead mechanism to consider its impact
on future frames. Extensive experimental results confirm that
the proposed scheme, when integrated into a conditional learned
video codec, is able to adapt motion bit rate effectively, showing
much improved rate-distortion performance particularly on test
sequences with complicated motion characteristics.
出版狀態Published - 12月 2022
事件2022 Picture Coding Symposium, PCS 2022 - San Jose, United States
持續時間: 7 12月 20229 12月 2022


Conference2022 Picture Coding Symposium, PCS 2022
國家/地區United States
城市San Jose


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