TY - JOUR
T1 - Unified Cross-Component Linear Model in VVC Based on a Subset of Neighboring Samples
AU - Huo, Junyan
AU - Du, Hongqing
AU - Li, Xinwei
AU - Wan, Shuai
AU - Yuan, Hui
AU - Ma, Yanzhuo
AU - Yang, Fuzheng
N1 - Publisher Copyright:
© 2005-2012 IEEE.
PY - 2022/12/1
Y1 - 2022/12/1
N2 - To compress industrial video content efficiently, H.266/Versatile Video Coding (VVC) introduces cross-component linear model (CCLM) prediction as a new coding tool, in which chroma components are predicted from the luma component based on a linear model. In this article, we propose a subset-based CCLM (S-CCLM), in which the model parameters are derived based on a subset of neighboring samples. To choose the most proper subset, we build the relationship between the prediction error and the geometric distance and resolve the optimal subset construction problem by minimizing the geometric distance. With the well-designed subset, a weight-guided parameter derivation algorithm is further proposed to improve the accuracy of the model parameters. The experimental results show that the proposed S-CCLM can achieve Bjontegaard delta bitrate (BD-rate) reductions of 0.14%, 0.64%, and 0.75% for the Y, Cb, and Cr components, respectively, when the number of samples in the subset, N, is 4 and BD-rate reductions of 0.22%, 0.80%, and 0.95% when N is 8. Given a small fixed N, fewer memory access operations are needed during the CCLM calculation, and a unified CCLM process can be achieved for coding blocks with different sizes and different modes. Due to its hardware-friendly architecture, the S-CCLM has been partially adopted by H.266/VVC.
AB - To compress industrial video content efficiently, H.266/Versatile Video Coding (VVC) introduces cross-component linear model (CCLM) prediction as a new coding tool, in which chroma components are predicted from the luma component based on a linear model. In this article, we propose a subset-based CCLM (S-CCLM), in which the model parameters are derived based on a subset of neighboring samples. To choose the most proper subset, we build the relationship between the prediction error and the geometric distance and resolve the optimal subset construction problem by minimizing the geometric distance. With the well-designed subset, a weight-guided parameter derivation algorithm is further proposed to improve the accuracy of the model parameters. The experimental results show that the proposed S-CCLM can achieve Bjontegaard delta bitrate (BD-rate) reductions of 0.14%, 0.64%, and 0.75% for the Y, Cb, and Cr components, respectively, when the number of samples in the subset, N, is 4 and BD-rate reductions of 0.22%, 0.80%, and 0.95% when N is 8. Given a small fixed N, fewer memory access operations are needed during the CCLM calculation, and a unified CCLM process can be achieved for coding blocks with different sizes and different modes. Due to its hardware-friendly architecture, the S-CCLM has been partially adopted by H.266/VVC.
KW - Cross-component linear model (CCLM)
KW - H.266
KW - subset construction
KW - unified derivation
KW - versatile video coding (VVC)
KW - video coding
UR - http://www.scopus.com/inward/record.url?scp=85124837111&partnerID=8YFLogxK
U2 - 10.1109/TII.2022.3151746
DO - 10.1109/TII.2022.3151746
M3 - 文章
AN - SCOPUS:85124837111
SN - 1551-3203
VL - 18
SP - 8654
EP - 8663
JO - IEEE Transactions on Industrial Informatics
JF - IEEE Transactions on Industrial Informatics
IS - 12
ER -