Convolutional Neural Networks for Video Intra Prediction Using Cross-component Adaptation

Recently, neural networks were shown to improve video and image intra prediction significantly. In this paper, the properties of different architectures for neural network-based intra prediction are evaluated. This includes an analysis of the properties of convolutional neural networks used for this purpose, showing that they outperform fully connected ones especially for complex and low resolution content. Also, the usage of separate networks for luma and chroma prediction, which are able to perform a learned cross-component prediction, is proposed as this is clearly beneficial for the prediction quality. Furthermore, a new way of signaling a neural network-based intra prediction mode in HEVC is investigated. In total this improves the compression performance in terms of average BD-rate changes by -2.0% for the luma and by -1.5% for the chroma channels.