  Video compression is a basic requirement for consumer and professional video
applications alike. Video coding standards such as H.264/AVC and H.265/HEVC are
widely deployed in the market to enable efficient use of bandwidth and storage
for many video applications. To reduce the coding artifacts and improve the
compression efficiency, neural network based loop filtering of the
reconstructed video has been developed in the literature. However, loop
filtering is a challenging task due to the variation in video content and
sampling densities. In this paper, we propose a on-line scaling based
multi-density attention network for loop filtering in video compression. The
core of our approach lies in several aspects: (a) parallel multi-resolution
convolution streams for extracting multi-density features, (b) single attention
branch to learn the sample correlations and generate mask maps, (c) a
channel-mutual attention procedure to fuse the data from multiple branches, (d)
on-line scaling technique to further optimize the output results of network
according to the actual signal. The proposed multi-density attention network
learns rich features from multiple sampling densities and performs robustly on
video content of different resolutions. Moreover, the online scaling process
enhances the signal adaptability of the off-line pre-trained model.
Experimental results show that 10.18% bit-rate reduction at the same video
quality can be achieved over the latest Versatile Video Coding (VVC) standard.
The objective performance of the proposed algorithm outperforms the
state-of-the-art methods and the subjective quality improvement is obvious in
terms of detail preservation and artifact alleviation.
