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.

中文翻译：

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使用基于注意力的 CNN 架构的色度帧内预测

神经网络可用于视频编码以改善色度帧内预测。特别是，与传统线性模型相比，全连接网络的使用能够更好地进行交叉组件预测。尽管如此，最先进的架构往往会忽略预测过程中各个参考样本的位置。本文提出了一种用于跨组件帧内预测的新神经网络架构。该网络使用一种新颖的注意力模块来对参考样本和预测样本之间的空间关系进行建模。所提出的方法被集成到多功能视频编码 (VVC) 预测管道中。实验结果表明，与基于神经网络的最先进的色度帧内预测方法相比，最新的 VVC 锚点具有压缩增益。