TY - GEN
T1 - Luma-Attention-based Chroma Intra Prediction for Versatile Video Coding
AU - Kim, Bumyoon
AU - Kim, Yongseong
AU - Jeong, Hyunki
AU - Jeon, Byeungwoo
N1 - Publisher Copyright:
© 2025 IEEE.
PY - 2025
Y1 - 2025
N2 - The remarkable advancements in machine learning are impacting most areas of research, and video coding is no exception. In this paper, we study a neural network-based chroma intra prediction technique which utilizes weighted coefficients obtained only from luma attention information. Experimental results show coding gains of 0.45%, 1.46%, and 1.28% for the Y, Cb, and Cr channels compared to VVC Test Model (VTM) version 23.0. These results highlight potential of chroma prediction solely from luma information as a novel approach to chroma intra prediction.
AB - The remarkable advancements in machine learning are impacting most areas of research, and video coding is no exception. In this paper, we study a neural network-based chroma intra prediction technique which utilizes weighted coefficients obtained only from luma attention information. Experimental results show coding gains of 0.45%, 1.46%, and 1.28% for the Y, Cb, and Cr channels compared to VVC Test Model (VTM) version 23.0. These results highlight potential of chroma prediction solely from luma information as a novel approach to chroma intra prediction.
KW - Intra Chroma Prediction
KW - Neural Network-based Video Coding
KW - VVC
UR - https://www.scopus.com/pages/publications/105018184006
U2 - 10.1109/BMSB65076.2025.11165564
DO - 10.1109/BMSB65076.2025.11165564
M3 - Conference contribution
AN - SCOPUS:105018184006
T3 - IEEE International Symposium on Broadband Multimedia Systems and Broadcasting, BMSB
BT - 2025 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting, BMSB 2025
PB - IEEE Computer Society
T2 - 20th IEEE International Symposium on Broadband Multimedia Systems and Broadcasting, BMSB 2025
Y2 - 11 June 2025 through 13 June 2025
ER -