Prevalent predictive coding-based video compression methods rely on a heavy encoder to reduce temporal redundancy, which makes it challenging to deploy them on resource-constrained devices. Since the 1970s, distributed source coding theory has indicated that independent encoding and joint decoding with side information (SI) can achieve high-efficient compression of correlated sources. This has inspired a distributed coding architecture aiming at reducing the encoding complexity. However, traditional distributed coding methods suffer from a substantial performance gap to predictive coding ones. Inspired by the great success of learning-based compression, we propose the first end-to-end distributed deep video compression framework to improve the rate-distortion performance. A key ingredient is an effective SI generation module at the decoder, which helps to effectively exploit inter-frame correlations without computation-intensive encoder-side motion estimation and compensation. Experiments show that our method significantly outperforms conventional distributed video coding and H.264. Meanwhile, it enjoys 6-7x encoding speedup against DVC [1] with comparable compression performance. Code is released at https://github.com/Xinjie-Q/Distributed-DVC.

翻译：基于预测编码的主流视频压缩方法依赖繁重的编码器来减少时间冗余，这使得此类方法难以部署在资源受限设备上。自20世纪70年代起，分布式信源编码理论指出，利用边信息进行独立编码与联合解码可实现相关信源的高效压缩，这一理论催生了旨在降低编码复杂度的分布式编码架构。然而，传统分布式编码方法相较预测编码方法存在明显的性能差距。受基于学习的压缩方法巨大成功的启发，我们提出了首个端到端分布式深度视频压缩框架，以提升率失真性能。其关键要素在于解码端设计的有效边信息生成模块——该模块无需计算量密集的编码端运动估计与补偿，即可高效利用帧间相关性。实验表明，本方法显著优于传统分布式视频编码与H.264标准。同时，与DVC [1]相比，本方法在保持可比较压缩性能的基础上，编码速度提升了6-7倍。代码已发布于https://github.com/Xinjie-Q/Distributed-DVC。