H.266跨分量线性模型预测的研究与改进

Research and improvement of H.266 cross-component linear model prediction

针对多功能视频编码(versatile video coding,VVC)帧内预测中的跨分量线性模型(cross-component linear model,CCLM)计算复杂度高的问题,本文提出了一种基于CCLM技术的改进算法QCCLM(quick cross-component linear model)。首先复制相邻可用样本填充不可用样本,来固定子采样样本的位置和数量,去除冗余过程和额外的计算步骤;然后对亮度下采样过程进行优化,减少下采样滤波器的种类;最后对线性模型参数β的推导过程进行改进,带来更精确的预测模型。实验结果表明,与H.266的标准算法相比,在全I帧的配置下,测试序列的色度分量平均节省了0.14%的码率,编码总时间平均降低了4.05%,该算法提高编码性能的同时降低了编码复杂度。

Aiming at the problem of the high computational complexity of the cross-component linear model(CCLM)in versatile video coding(VVC)intra prediction,this paper proposes an improved algorithm,quick cross-component linear model(QCCLM)based on the CCLM technology.First,the position and number of sub-sampling samples are fixed according to copy adjacent available samples to fill unavailable samples,and remove the redundant processes and additional calculation steps;Then,the luminance down-sampling optimization process is used to reduce the types of down-sampling filters;Finally,the derivation process of the linear model parameterβis improved so as to make the prediction model more accurate.The experimental results show that compared with the standard algorithm of H.266,the algorithm saves 0.14%of the code rate on the chrominance component of the test image sequence in the all intra frames configuration,and the total coding time is reduced by 4.05%on average.The algorithm improves coding performance while reducing coding complexity.