Deep degradation-aware up-sampling-based depth video coding

Abstract

The smooth regions in depth videos contain a significant proportion of homogeneous content, resulting in many spatial redundancies. To improve the coding efficiency of depth videos, this paper proposes a deep degradation-aware up-sampling-based depth video coding method. For reducing spatial redundancies effectively, the proposed method compresses the depth video at a low resolution, and restores the resolution by utilizing the learning-based up-sampling technology. To recover high-quality depth videos, a degradation-aware up-sampling network is proposed, which explores the degradation information of compression artifacts and sampling artifacts to restore the resolution. Specifically, the compression artifact removal module is used to obtain refined low-resolution depth frames by learning the representation of compression artifacts. Meanwhile, a jointly optimized learning strategy is designed to enhance the capability of recovering high-frequency details, which is beneficial for up-sampling. According to the experimental results, the proposed method achieves considerable performance in depth video coding compared with 3D-HEVC.