Learning for Video Compression

IEEE Transactions on Circuits and Systems for Video Technology, pp. 566-576, 2020.

Cited by: 32|Bibtex|Views31|DOI:https://doi.org/10.1109/TCSVT.2019.2892608
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Other Links: dblp.uni-trier.de|academic.microsoft.com|arxiv.org

Abstract:

One key challenge to learning-based video compression is that motion predictive coding, a very effective tool for video compression, can hardly be trained into a neural network. In this paper, we propose the concept of Pixel-MotionCNN (PMCNN) which includes motion extension and hybrid prediction networks. PMCNN can model spatiotemporal co...More

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