Residual-Guided In-Loop Filter Using Convolution Neural Network

TL;DR

This paper introduces RRNet, a neural network that leverages residual information from bitstreams to improve video coding efficiency, significantly reducing bitrate while maintaining quality.

Contribution

The novel RRNet utilizes residual signals from the compression pipeline as additional input, enhancing neural network-based in-loop filtering for better video reconstruction.

Findings

Achieves significant BD-rate savings over HEVC.

Outperforms existing CNN-based filtering schemes.

Utilizes residual features for adaptive filtering.

Abstract

The block-based coding structure in the hybrid video coding framework inevitably introduces compression artifacts such as blocking, ringing, etc. To compensate for those artifacts, extensive filtering techniques were proposed in the loop of video codecs, which are capable of boosting the subjective and objective qualities of reconstructed videos. Recently, neural network based filters were presented with the power of deep learning from a large magnitude of data. Though the coding efficiency has been improved from traditional methods in High-Efficiency Video Coding (HEVC), the rich features and information generated by the compression pipeline has not been fully utilized in the design of neural networks. Therefore, in this paper, we propose the Residual-Reconstruction-based Convolutional Neural Network (RRNet) to further improve the coding efficiency to its full extent, where the compression features induced from bitstream in form of prediction residual is fed into the network as an additional input to the reconstructed frame. In essence, the residual signal can provide valuable information about block partitions and can aid reconstruction of edge and texture regions in a picture. Thus, more adaptive parameters can be trained to handle different texture characteristics. The experimental results show that our proposed RRNet approach presents significant BD-rate savings compared to HEVC and the state-of-the-art CNN-based schemes, indicating that residual signal plays a significant role in enhancing video frame reconstruction.