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

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Document pages: 5 pages

Abstract: Conventional video compression methods employ a linear transform and blockmotion model, and the steps of motion estimation, mode and quantizationparameter selection, and entropy coding are optimized individually due tocombinatorial nature of the end-to-end optimization problem. Learned videocompression allows end-to-end rate-distortion optimized training of allnonlinear modules, quantization parameter and entropy model simultaneously.While previous work on learned video compression considered training asequential video codec based on end-to-end optimization of cost averaged overpairs of successive frames, it is well-known in conventional video compressionthat hierarchical, bi-directional coding outperforms sequential compression. Inthis paper, we propose for the first time end-to-end optimization of ahierarchical, bi-directional motion compensated learned codec by accumulatingcost function over fixed-size groups of pictures (GOP). Experimental resultsshow that the rate-distortion performance of our proposed learnedbi-directional { it GOP coder} outperforms the state-of-the-art end-to-endoptimized learned sequential compression as expected.

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