Enhanced Derivation of Model Parameters for Cross-Component Linear Model (CCLM) in VVC

Yong-Uk YOON  Do-Hyeon PARK  Jae-Gon KIM  

IEICE TRANSACTIONS on Information and Systems   Vol.E103-D   No.2   pp.469-471
Publication Date: 2020/02/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2019EDL8045
Type of Manuscript: LETTER
Category: Image Processing and Video Processing
JVET,  VVC,  chroma intra prediction,  CCLM,  

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Cross-component linear model (CCLM) has been recently adopted as a chroma intra-prediction tool in Versatile Video Coding (VVC), which is being developed as a new video coding standard. CCLM predicts chroma components from luma components through a linear model based on assumption of linear correlation between both components. A linear model is derived from the reconstructed neighboring luma and chroma samples of the current coding block by linear regression. A simplified linear modeling method recently adopted in the test model of VVC (VTM) 3.0 significantly reduces computational complexity of deriving model parameters with considerable coding loss. This letter proposes a method of linear modeling to compensate the coding loss of the simplified linear model. In the proposed method, the model parameters which are quite roughly derived in the existing simplified linear model are refined more accurately using individual method to derive each parameter. Experimental results show that, compared to VTM 3.0, the proposed method gives 0.08%, 0.52% and 0.55% Bjotegaard-Delta (BD)-rate savings, for Y, Cb and Cr components, respectively, in the All-Intra (AI) configuration with negligible computational complexity increase.