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.

中文翻译：

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视频压缩中用于环路滤波的多密度注意力网络

视频压缩是消费者和专业视频应用程序的基本要求。诸如H.264 / AVC和H.265 / HEVC之类的视频编码标准已在市场中广泛部署，以使许多视频应用能够有效利用带宽和存储。为了减少编码伪像并提高压缩效率，文献中已经开发了基于神经网络的重构视频的环路滤波。然而，由于视频内容和采样密度的变化，环路滤波是一项具有挑战性的任务。在本文中，我们提出了一种基于在线缩放的多密度注意力网络，用于视频压缩中的环路滤波。我们方法的核心在于几个方面：（a）用于提取多密度特征的并行多分辨率卷积流，（b）单关注分支以学习样本相关性并生成掩码图;（c）通道相互关注程序以融合来自多个分支的数据;（d）在线缩放技术以进一步优化网络的输出结果到实际信号。所提出的多密度注意力网络从多个采样密度中学习了丰富的功能，并在不同分辨率的视频内容上表现出色。此外，在线缩放过程增强了离线预训练模型的信号适应性。实验结果表明，与最新的多功能视频编码（VVC）标准相比，在相同视频质量下可以实现10.18％的比特率降低。