The data storage has been one of the bottlenecks in surveillance systems. The conventional video compression algorithms such as H.264 and H.265 do not fully utilize the low information density characteristic of the surveillance video. In this paper, we propose a video compression method that extracts and compresses the foreground and background of the video separately. The compression ratio is greatly improved by sharing background information among multiple adjacent frames through an adaptive background updating and interpolation module. Besides, we present two different schemes to compress the foreground and compare their performance in the ablation study to show the importance of temporal information for video compression. In the decoding end, a coarse-to-fine two-stage module is applied to achieve the composition of the foreground and background and the enhancements of frame quality. Furthermore, an adaptive sampling method for surveillance cameras is proposed, and we have shown its effects through software simulation. The experimental results show that our proposed method requires 69.5% less bpp (bits per pixel) than the conventional algorithm H.265 to achieve the same PSNR (36 dB) on the HECV dataset.

中文翻译：

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一种用于监控视频的带有残差编码的前景-背景并行压缩

数据存储一直是监控系统的瓶颈之一。H.264、H.265等传统视频压缩算法没有充分利用监控视频信息密度低的特点。在本文中，我们提出了一种视频压缩方法，将视频的前景和背景分别提取和压缩。通过自适应背景更新和插值模块在多个相邻帧之间共享背景信息，大大提高了压缩率。此外，我们提出了两种不同的方案来压缩前景并比较它们在消融研究中的性能，以显示时间信息对视频压缩的重要性。在解码端，采用由粗到细的两阶段模块实现前景和背景的合成和帧质量的增强。此外，提出了一种监控摄像机的自适应采样方法，并通过软件仿真展示了其效果。实验结果表明，我们提出的方法需要比传统算法 H.265 少 69.5% 的 bpp（每像素位数）才能在 HECV 数据集上实现相同的 PSNR (36 dB)。