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About this paper
Chih-Hsuan Lin, Yi-Hsin Chen, Wen-Hsiao Peng
Researchers at National Yang Ming Chiao Tung University introduce a content-adaptive motion rate adaptation scheme for learned video compression, employing a patch-level "alpha-map" to dynamically balance motion and inter-frame coding bit rates. This approach improves rate-distortion performance by 2-5% on various test sequences, particularly those with complex motion, by optimizing bit allocation at inference time.
Key insights
- Spatially-adaptive bit allocation between motion and inter-frame coding, guided by a patch-level ":alpha-map", effectively adapts to varying content complexities.
- Online optimization of coding parameters during inference enables learned codecs to better generalize to unseen video sequences by bridging the domain gap.
- Incorporating a look-ahead mechanism in the ":alpha-map" optimization process can further enhance rate-distortion performance by considering future frame dependencies.
Summary by alphaXiv.
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