Motion-aware deep video coding network
Rida Khan, Ying Liu · 2020
Recent advances in deep learning have achieved great success in fundamental computer vision tasks such as classification, detection and segmentation. Nevertheless, the research effort in deep learning-based video coding is still in its infancy. State-of-the-art deep video coding networks explore temporal correlations by means of frame-level motion estimation and motion compensation, which require high computational complexity due to the frame size, while existing block-level interframe prediction schemes utilize only the co-located blocks in preceding frames, which did not consider object motions. In this work, we propose a novel motion-aware deep video coding network, in which inter-frame correlations are effectively explored via a block-level motion compensation network. Experimental results demonstrate that the proposed inter-frame deep video coding model significantly improves the decoding quality under the same compression ratio.