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Spatial Information Refinement for Chroma Intra Prediction in Video Coding | IEEE Conference Publication | IEEE Xplore

Spatial Information Refinement for Chroma Intra Prediction in Video Coding


Abstract:

Video compression benefits from advanced chroma intra prediction methods, such as the Cross-Component Linear Model (CCLM) which uses linear models to approximate the rela...Show More

Abstract:

Video compression benefits from advanced chroma intra prediction methods, such as the Cross-Component Linear Model (CCLM) which uses linear models to approximate the relationship between the luma and chroma components. Recently it has been proven that advanced cross-component prediction methods based on Neural Networks (NN) can bring additional coding gains. In this paper, spatial information refinement is proposed for improving NN-based chroma intra prediction. Specifically, the performance of chroma intra prediction can be improved by refined down-sampling or by incorporating location information. Experimental results show that the two proposed methods obtain 0.31%, 2.64%, 2.02% and 0.33%, 3.00%, 2.12% BD-rate reduction on Y, Cb and Cr components, respectively, under All-Intra configuration, when implemented in Versatile Video Coding (H.266/VVC) test model.
Date of Conference: 14-17 December 2021
Date Added to IEEE Xplore: 03 February 2022
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Conference Location: Tokyo, Japan

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