TY - GEN
T1 - Spatial Information Refinement for Chroma Intra Prediction in Video Coding
AU - Zou, Chengyi
AU - Wan, Shuai
AU - Ji, Tiannan
AU - Mrak, Marta
AU - Blanch, Marc Gorriz
AU - Herranz, Luis
N1 - Publisher Copyright:
© 2021 APSIPA.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
KW - Chroma intra prediction
KW - convolutional neural networks
KW - spatial information refinement
UR - http://www.scopus.com/inward/record.url?scp=85126700432&partnerID=8YFLogxK
M3 - 会议稿件
AN - SCOPUS:85126700432
T3 - 2021 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2021 - Proceedings
SP - 1422
EP - 1427
BT - 2021 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2021 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2021
Y2 - 14 December 2021 through 17 December 2021
ER -