Hierarchical motion-compensated deep network for video compression

Video coding is the process of reducing the huge volume of video data to a small number of bits. High coding efficiency reduces the bandwidth required for video streaming, and the space required to store the video data on electronic devices, while maintaining the fidelity of the decompressed video signal. In recent years, deep learning has been extensively applied in the field of video coding. However, it remains challenging how to explore the intra- and inter-frame correlations in deep learning-based video coding systems to improve the coding efficiency. In this work, we propose a hierarchical motion estimation and compensation network for video compression. The video frames are tagged as intra-frames and inter-frames. While intra-frames are compressed independently, the inter-frames are hierarchically predicted by adjacent frames using a bi-directional motion prediction network, which results in highly sparse and compressible residue. The residue frames are then compressed via separately trained residue coding networks. Experimental results demonstrate that the proposed hierarchical deep video compression network offers significantly higher coding efficiencey and superior visual quality compared to prior arts.