End-to-end Distributed Video Coding

Abstract

Existing distributed video coding (DVC) frameworks use manually designed and optimized modules when encoding and decoding video. Each module can set the appropriate parameters as much as possible to achieve its independent optimization. But there is no connection between the modules, and overall end-to-end optimization is not realized. Inspired by the application of neural networks to video coding, we try to implement DVC using neural networks. This article proposes an end-to-end DVC framework, which combines DVC and neural networks to perform end-to-end encoding and decoding of Wyner-Ziv (WZ) frames, while achieving variable compression rates. We employ four different kinds of side information to assist in decoding the WZ frames. Experimental results demonstrate that the proposed framework achieves excellent decoded performance on videos with varying degrees of motion intensity.