Dense Residual Convolutional Neural Network based In-Loop Filter for HEVC

Abstract

In-loop filtering is a key technique in the state of art video coding standard that plays a significant role in suppressing compression artifacts. Motivated by the latest advances of deep learning, in this paper, we design a dense residual convolutional neural network (DRN) based in-loop filter for High Efficiency Video Coding (HEVC). Taking advantage of both dense shortcuts and residual learning, DRN efficiently exploits the multi-level features to restore the high quality image from the degraded one. Bottleneck layers are employed in DRN in order to adaptively fuse the hierarchical features and saving the computational resources at the same time. Experimental results show that the proposed DRN based in-loop filter can further boost the coding performance, which provides 6.9% BD-rate reduction on average compared to the HEVC baseline. In addition, the proposed DRN outperforms previous CNN based in-loop filters.