  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).
