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Enhancing HEVC Spatial Prediction by Context-based Learning

Abstract : Deep generative models have been recently employed to compress images, image residuals or to predict image regions. Based on the observation that state-of-the-art spatial prediction is highly optimized from a rate-distortion point of view, in this work we study how learning-based approaches might be used to further enhance this prediction. To this end, we propose an encoder-decoder convolutional network able to reduce the energy of the residuals of HEVC intra prediction, by leveraging the available context of previously decoded neighboring blocks. The proposed context-based prediction enhancement (CBPE) scheme enables to reduce the mean square error of HEVC prediction by 25% on average, without any additional signalling cost in the bitstream.
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Contributor : Giuseppe Valenzise Connect in order to contact the contributor
Submitted on : Thursday, March 5, 2020 - 11:37:40 AM
Last modification on : Wednesday, November 3, 2021 - 6:23:00 AM
Long-term archiving on: : Saturday, June 6, 2020 - 2:30:09 PM


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  • HAL Id : hal-02243361, version 1


Li Wang, Attilio Fiandrotti, Andrei Purica, Giuseppe Valenzise, Marco Cagnazzo. Enhancing HEVC Spatial Prediction by Context-based Learning. ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing, May 2019, Brighton, United Kingdom. pp.4035-4039. ⟨hal-02243361⟩



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