Paper Info
Title
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 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.
Year
Venue
Keywords
2021
2021 ASIA-PACIFIC SIGNAL AND INFORMATION PROCESSING ASSOCIATION ANNUAL SUMMIT AND CONFERENCE (APSIPA ASC)
Chroma intra prediction, convolutional neural networks, spatial information refinement
DocType
ISSN
Citations 
Conference
2309-9402
0
PageRank 
References 
Authors
0.34
0
6
Name
Order
Citations
PageRank
Chengyi Zou100.34
Shuai Wan212322.20
Tiannan Ji300.34
Marta Mrak418834.92
Marc Gorriz Blanch501.69
Luis Herranz619426.17