Communication Dans Un Congrès Année : 2019

Enhancing HEVC Spatial Prediction by Context-based Learning

Résumé

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.

Fichier principal
Vignette du fichier
icassp2019li.pdf (616.76 Ko) Télécharger le fichier
Origine Fichiers produits par l'(les) auteur(s)
Licence
Loading...

Dates et versions

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

Licence

Identifiants

Citer

Li Wang, Attilio Fiandrotti, Andrei Purica, Giuseppe Valenzise, Marco Cagnazzo. Enhancing HEVC Spatial Prediction by Context-based Learning. 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2019), May 2019, Brighton, United Kingdom. pp.4035-4039, ⟨10.1109/icassp.2019.8683624⟩. ⟨hal-02243361⟩
266 Consultations
447 Téléchargements

Altmetric

Partager

  • More