  Neural networks can be used in video coding to improve chroma
intra-prediction. In particular, usage of fully-connected networks has enabled
better cross-component prediction with respect to traditional linear models.
Nonetheless, state-of-the-art architectures tend to disregard the location of
individual reference samples in the prediction process. This paper proposes a
new neural network architecture for cross-component intra-prediction. The
network uses a novel attention module to model spatial relations between
reference and predicted samples. The proposed approach is integrated into the
Versatile Video Coding (VVC) prediction pipeline. Experimental results
demonstrate compression gains over the latest VVC anchor compared with
state-of-the-art chroma intra-prediction methods based on neural networks.
