Improving distributed video coding with deep learning

Abstract

In the current state-of-the-art distributed video coding (DVC) solutions, side information (SI) frames are created by motion-compensated interpolation. This is the same technique used by frame rate upconversion operations. Recently, these operations are accomplished using deep learning (DL), with significantly improved outcomes. DL is also used to improve the decoded images of numerous compression methods. Consequently, it is natural to wonder what are the impacts of these enhancements on DVC. In this regard, we employed DL networks to create SI and enhance the quality of the DVC decoded frames. We show how these two operations were applied to DVC and their effects on rate-distortion (RD) performance. The comparison with state-of-the-art DVC systems shows an improvement in the quality of the SI frames and that of the decoded frames, and thus an improvement of the RD performance of DVC.