1 research outputs found
A Foreground-background Parallel Compression with Residual Encoding for Surveillance Video
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