Optimized Spatial Recurrent Network for Intra Prediction in Video Coding

Intra prediction in modern video codecs is able to efficiently reduce spatial redundancy in video frames. With preceding pixels as context, traditional intra prediction schemes generate linear predictions based on several predefined directions (i.e. modes) for the current prediction unit (PU). However, these modes are relatively simple and are not able to handle complex textures, which leads to additional bits encoding the residue. In this paper, we design a convolutional neural network (CNN) guided spatial recurrent neural network (RNN) to improve the intra prediction in High-Efficiency Video Coding (HEVC). By exploring the correlations between pixels, the network learns to generate prediction signal in a progressive manner. The progressive model solves the problem of asymmetry in intra prediction naturally. As the model is designed for global context modeling, no flags for intra prediction modes selection need to be encoded. Our proposed intra prediction scheme achieves on average 1.2% bit-rate saving compared with HEVC.