A Video Post-Filter Deblocking Method Based on Temporal Boosting Residual Networks

Jianyu Wang, Shaohui Liu, Feng Jiang, Xiaoshuai Sun, Yongliang Liu

Abstract: Block-based hybrid coding is widely used in video compression. As the bit rate decreases, the quantization becomes lossy resulting in unacceptable blocking artifacts. Although advanced video encoder with loop filter has achieved promising results, the problem still remains unsolved. Most of the previous solutions regard a video sequence as a group of independent frames, without considering their temporal relationships. To address the above issue, this paper designs a temporal boosting residual network aiming to integrate the temporal information and the structural information. And the information is represented as the residual values of previous frames which are extracted by the deep residual network. The entire framework is a cascading architecture that implies a coarse-to-fine processing. The proposed solution does not modify any module of the codec, it only takes the lossy frames as input, and outputs the enhanced frames, which is a typical end-to-end mapping. Experimental results validate that our framework has achieved 0.6-1.0 dB improvement on average based on HEVC software and outperformed the state-of-the-art methods in both objective and perceptual quality.