Cross-component linear model (CCLM) prediction has been repeatedly proven to be effective in reducing the inter-channel redundancies in video compression. Essentially speaking, the linear model is identically trained by employing accessible luma and chroma reference samples at both encoder and decoder, elevating the level of operational complexity due to the least square regression or max-min based model parameter derivation. In this paper, we investigate the capability of the linear model in the context of sub-sampled based cross-component correlation mining, as a means of significantly releasing the operation burden and facilitating the hardware and software design for both encoder and decoder. In particular, the sub-sampling ratios and positions are elaborately designed by exploiting the spatial correlation and the inter-channel correlation. Extensive experiments verify that the proposed method is characterized by its simplicity in operation and robustness in terms of rate-distortion performance, leading to the adoption by Versatile Video Coding (VVC) standard and the third generation of Audio Video Coding Standard (AVS3).

中文翻译：

---

新兴视频编码标准的子采样交叉分量预测

交叉分量线性模型 (CCLM) 预测已被反复证明可有效减少视频压缩中的通道间冗余。从本质上讲，线性模型通过在编码器和解码器处使用可访问的亮度和色度参考样本进行相同的训练，由于最小二乘回归或基于最大-最小的模型参数推导而提高了操作复杂性的水平。在本文中，我们研究了线性模型在基于子采样的交叉分量相关挖掘的背景下的能力，作为一种显着释放操作负担并促进编码器和解码器的硬件和软件设计的手段。特别是，通过利用空间相关性和通道间相关性，精心设计了子采样率和位置。