Content-Adaptive Motion Rate Adaption For Learned Video Compression

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

Research output: Contribution to conferencePaperpeer-review


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.
Original languageEnglish
Number of pages5
StatePublished - Dec 2022
Event2022 Picture Coding Symposium, PCS 2022 - San Jose, United States
Duration: 7 Dec 20229 Dec 2022


Conference2022 Picture Coding Symposium, PCS 2022
Country/TerritoryUnited States
CitySan Jose


Dive into the research topics of 'Content-Adaptive Motion Rate Adaption For Learned Video Compression'. Together they form a unique fingerprint.

Cite this