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CNN-based Intra-Prediction for Lossless HEVC This publication appears in: IEEE Transactions on Circuits and Systems for Video Technology Authors: I. Schiopu, H. Huang and A. Munteanu Volume: 2019 Pages: 1-13 Publication Year: 2019
Abstract: The paper proposes a novel block-wise prediction paradigm based on Convolutional Neural Networks (CNN) for lossless video coding. A deep neural network model which follows a multi-resolution design is employed for block-wise prediction. A set of novel contributions is proposed to improve the 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.
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