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A Hybrid Neural Network For Chroma Intra Prediction

Yue Li, Li Li, Zhu Li, Jianchao Yang, Ning Xu, Dong Liu, Houqiang Li
EventICIP 2018
Research AreasDeep Learning

Abstract: For chroma intra prediction, previous methods exemplified by the Linear Model method (LM) usually assume a linear correlation between the luma and chroma components in a coding block. This assumption is inaccurate for complex image content or large blocks, and restricts the prediction accuracy. In this paper, we propose a chroma intra prediction method by exploiting both spatial and cross-channel correlations using a hybrid neural network. Specifically, we utilize a convolutional neural network to extract features from the reconstructed luma samples of the current block, as well as utilize a fully connected network to extract features from the neighboring reconstructed luma and chroma samples. The extracted twofold features are then fused to predict the chroma samples–Cb and Cr simultaneously. The proposed chroma intra prediction method is integrated into HEVC. Preliminary results show that, compared with HEVC plus LM, the proposed method achieves on average 0.2%, 3.1% and 2.0% BDrate reduction on Y, Cb and Cr components, respectively, under All-Intra configuration.

Index Terms: Chroma intra prediction, convolutional neural network, fully connected network, hybrid neural network.

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