Enhancing HEVC Spatial Prediction by Context-based Learning

Li Wang; Attilio Fiandrotti; Andrei I. Purica; Giuseppe Valenzise; Marco Cagnazzo

ICASSP 2019accepted0 citations

Enhancing HEVC Spatial Prediction by Context-based Learning

Li Wang, Attilio Fiandrotti, Andrei I. Purica, Giuseppe Valenzise, Marco Cagnazzo

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 neigh-boring 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.

BibTeX

@inproceedings{icassp2019_enhancinghevcspa,
  title = {Enhancing HEVC Spatial Prediction by Context-based Learning},
  author = {Li Wang and Attilio Fiandrotti and Andrei I. Purica and Giuseppe Valenzise and Marco Cagnazzo},
  booktitle = {ICASSP 2019},
  year = {2019}
}