Motion-aware deep video coding network

Rida Khan; Ying Liu

doi:10.1117/12.2560814

21 April 2020 Motion-aware deep video coding network

Rida Khan, Ying Liu

Proceedings Volume 11395, Big Data II: Learning, Analytics, and Applications; 113950B (2020) https://doi.org/10.1117/12.2560814
Event: SPIE Defense + Commercial Sensing, 2020, Online Only

Abstract

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.

Citation Download Citation

Rida Khan and Ying Liu "Motion-aware deep video coding network", Proc. SPIE 11395, Big Data II: Learning, Analytics, and Applications, 113950B (21 April 2020); https://doi.org/10.1117/12.2560814

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