Hierarchical Random Access Coding for Deep Neural Video Compression

Recently, neural video compression networks have obtained impressive results. However, previous neural video compression models mostly focus on low-delay configuration with the order of display being the same as the order of coding. In this paper, we propose a hierarchical random access coding approach that exploits bidirectionally temporal redundancy to improve the coding efficiency of existing deep neural video compression models. The proposed framework applies a video frame interpolation network to improve inter-frame prediction. In addition, a hierarchical coding structure is also proposed in this paper. Experimental results show the proposed framework improves the coding efficiency of the base deep neural model by 48.01% with the UVG dataset, 50.96% with the HEVC-class B dataset, and outperforms the previous deep neural video compression networks.