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Video compression benefits from advanced chroma intra prediction methods,such as the Cross-Component Linear Model (CCLM) which uses linear models toapproximate the relationship between the luma and chroma components. Recentlyit has been proven that advanced cross-component prediction methods based onNeural Networks (NN) can bring additional coding gains. In this paper, spatialinformation refinement is proposed for improving NN-based chroma intraprediction. Specifically, the performance of chroma intra prediction can beimproved by refined down-sampling or by incorporating location information.Experimental results show that the two proposed methods obtain 0.31%, 2.64%,2.02% and 0.33%, 3.00%, 2.12% BD-rate reduction on Y, Cb and Cr components,respectively, under All-Intra configuration, when implemented in VersatileVideo Coding (H.266/VVC) test model. Index Terms-Chroma intra prediction,convolutional neural networks, spatial information refinement.

Spatial Information Refinement for Chroma Intra Prediction in Video Coding