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

摘要:

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。

Abstract:

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 parameter β is 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.