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Conference Publication

Low-Complexity Angular Intra-Prediction Con-volutional Neural Network for Lossless HEVC

Host Publication: IEEE International Workshop on Multimedia Signal Processing

Authors: H. Huang, I. Schiopu and A. Munteanu

UsePubPlace: Tampere

Publication Date: Jul. 2020

Number of Pages: 6


Abstract:

The paper proposes a novel low-complexity Convolutional Neural Network (CNN) architecture for block-wise angular intra-prediction in lossless video coding. The proposed CNN architecture is designed based on an efficient patch processing layer structure. The proposed CNN-based prediction method is employed to process an input patch containing the causal neighborhood of the current block in order to directly generate the predicted block. The trained models are integrated in the HEVC video coding standard to perform CNN-based angular intra-prediction and to compete with the conventional HEVC prediction. The proposed CNN architecture contains a reduced number of parameters equivalent to only 37% of that of the state-of-the-art reference CNN architecture. Experimental results show that the inference runtime is also reduced by around 5.5% compared to that of the reference method. At the same time, the proposed coding systems yield 83% to 91% of the compression performance of the reference method. The results demonstrate the potential of structural and complexity optimizations in CNN-based intra-prediction for lossless HEVC.

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Dr. Ionut Schiopu

+32 (0)02 629 169

ischiopu@etrovub.be

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Prof. Dr. Ir. Adrian Munteanu

+32 (0)02 629 168

acmuntea@etrovub.be

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