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Enhancing HEVC Spatial Prediction by Context-based Learning

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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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Dates and versions

hal-02243361 , version 1 (05-03-2020)

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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, ⟨10.1109/icassp.2019.8683624⟩. ⟨hal-02243361⟩
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