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arXiv · 2008.05028

End-to-End Rate-Distortion Optimization for Bi-Directional Learned Video Compression

M. Akin Yilmaz·A. Murat Tekalp
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Abstract

Conventional video compression methods employ a linear transform and block motion model, and the steps of motion estimation, mode and quantization parameter selection, and entropy coding are optimized individually due to combinatorial nature of the end-to-end optimization problem. Learned video compression allows end-to-end rate-distortion optimized training of all nonlinear modules, quantization parameter and entropy model simultaneously. While previous work on learned video compression considered training a sequential video codec based on end-to-end optimization of cost averaged over pairs of successive frames, it is well-known in conventional video compression that hierarchical, bi-directional coding outperforms sequential compression. In this paper, we propose for the first time end-to-end optimization of a hierarchical, bi-directional motion compensated learned codec by accumulating cost function over fixed-size groups of pictures (GOP). Experimental results show that the rate-distortion performance of our proposed learned bi-directional {\it GOP coder} outperforms the state-of-the-art end-to-end optimized learned sequential compression as expected.

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M. Akin Yilmaz, A. Murat Tekalp. 2020-08-11. End-to-End Rate-Distortion Optimization for Bi-Directional Learned Video Compression. https://doi.org/10.1109/icip40778.2020.9190881

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