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CNN-Based Intra-Prediction for Lossless HEVC.
- Source :
-
IEEE Transactions on Circuits & Systems for Video Technology . Jul2020, Vol. 30 Issue 7, p1816-1828. 13p. - Publication Year :
- 2020
-
Abstract
- The paper proposes a novel block-wise prediction paradigm based on Convolutional Neural Networks (CNNs) for lossless video coding. A deep neural network model which follows a multi-resolution design is employed for block-wise prediction. Several contributions are proposed to improve neural network training. A first contribution proposes a novel loss function formulation for an efficient network training based on a new approach for patch selection. Another contribution consists in replacing all HEVC-based angular intra-prediction modes with a CNN-based intra-prediction method, where each angular prediction mode is complemented by a CNN-based prediction mode using a specifically trained model. Another contribution consists in an efficient adaptation of the CNN-based intra-prediction residual for lossless video coding. Experimental results on standard test sequences show that the proposed coding system outperforms the HEVC standard with an average bitrate improvement of around 5%. To our knowledge, the paper is the first to replace all the traditional HEVC-based angular intra-prediction modes with an intra-prediction method based on modern Machine Learning techniques for lossless video coding applications. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 10518215
- Volume :
- 30
- Issue :
- 7
- Database :
- Academic Search Index
- Journal :
- IEEE Transactions on Circuits & Systems for Video Technology
- Publication Type :
- Academic Journal
- Accession number :
- 144375885
- Full Text :
- https://doi.org/10.1109/TCSVT.2019.2940092