Learning for Video Compression with Hierarchical Quality and Recurrent Enhancement
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The recent years have witnessed the great potential of deep learning for video compression. In this paper, we propose the Hierarchical Learned Video Compression (HLVC) approach with three hierarchical quality layers and recurrent enhancement. To be specific, the frames in the first layer are compressed by image compression method with the highest quality. Using them as references, we propose the Bi-Directional Deep Compression (BDDC) network to compress the second layer with relatively high quality. Then, the third layer frames are compressed with the lowest quality, by the proposed Single Motion Deep Compression (SMDC) network, which adopts a single motion map to estimate the motions of multiple frames, thus saving the bit-rate for motion information. In our deep decoder, we develop the Weighted Recurrent Quality Enhancement (WRQE) network with the inputs of both compressed frames and bit stream. In the recurrent cell of WRQE, the memory and update signal are weighted by quality features to reasonably leverage multi-frame information for enhancement. In our HLVC approach, the hierarchical quality benefits the coding efficiency, since the high quality information facilitates the compression and enhancement of low quality frames at encoder and decoder sides, respectively. Finally, the experiments validate that our HLVC approach advances the state-of-the-art deep video compression methods, and outperforms x265 low delay P very fast mode in terms of both PSNR and MS-SSIM. The project page is at https://github.com/RenYang-home/HLVC.
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