Recursive Residual Convolutional Neural Network-Based In-Loop Filtering for Intra Frames

Abstract

Although the in-loop filtering incorporated in High Efficiency Video Coding (HEVC) standard improves the subjective quality of reconstructed pictures and increases the compression efficiency, it still cannot satisfy the demand for higher quality in the rapid growth of video usage. In this paper, we propose recursive residual convolution neural network (RRCNN)-based in-loop filtering to further improve the quality of reconstructed intra frames while reducing the bitrates. Specifically, RRCNN estimates the residual images between the compressed distorted images and original noncompressed ones, and there are shortcut connections that skip a few stacked layers in the structure of RRCNN to ease the training difficulty. By applying the same set of weights recursively, RRCNN achieves excellent performance while utilizing far fewer parameters. For concise in-loop filtering, we train a single model capable of handling various bitrate settings. Different networks for the filtering of luma and chroma components are designed respectively to better learn the filtering characteristics of different channels. Moreover, to fully adapt the various input videos and boost the performance, a coding tree unit (CTU) control flag is signaled to indicate the filtering method from the sense of rate-distortion optimization (RDO). Extensive experimental results show that our scheme achieves significant bitrate savings compared to HEVC, leading to on average 8.7% BD-rate reduction, with up to a 15.1% BD-rate reduction for luma, and more than 20% BD-rate reductions for chroma on average.