Prevalent predictive coding-based video compression methods rely on a heavy encoder to reduce the temporal redundancy, which makes it challenging to deploy them on resource-constrained devices. Meanwhile, as early as 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：当前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域冗余，这使得难以将其部署在资源受限设备上。与此同时，早在20世纪70年代，分布式信源编码理论已指出，对相关信源进行独立编码并利用边信息进行联合解码可实现高效压缩。这启发了一种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于预测编码方法存在显著的性能差距。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改善率失真性能。其核心在于解码器中设计了高效的边信息生成模块，该模块无需编码端进行计算密集型的运动估计与补偿即可有效利用帧间相关性。实验表明，本方法显著优于传统分布式视频编码及H.264编码标准。同时，在与DVC [1]相当的压缩性能下，编码速度提升6-7倍。代码已开源至https://github.com/Xinjie-Q/Distributed-DVC。