论文标题
具有基于注意的CNN体系结构的色度内部预测
Chroma Intra Prediction with attention-based CNN architectures
论文作者
论文摘要
神经网络可用于视频编码以改善色彩内预测。尤其是，完全连接的网络的使用使得有关传统线性模型的更好跨组件预测。尽管如此，最先进的体系结构倾向于在预测过程中忽略单个参考样本的位置。本文提出了一种新的神经网络体系结构，用于跨组件内预测。该网络使用新颖的注意模块来模拟参考和预测样本之间的空间关系。提出的方法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新色度预测方法相比，最新VVC锚的压缩增长。
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.
