运动很重要:压缩监控视频的新框架

Xiaojie Guo, Siyuan Li, Xiaochun Cao
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引用次数: 13

摘要

目前,视频监控在公共安全领域发挥着非常重要的作用。对于存储通常包含极长序列的视频,它需要巨大的空间。视频压缩技术可以在一定程度上缓解存储负荷,如H.264/AVC。然而,现有的编解码器并没有具体考虑到监控视频的特点,即监控视频背景具有密集的冗余性,因此在对监控视频进行编码时,其有效性和效率都不够高。本文介绍了一种压缩此类视频的新框架。我们首先基于少量观察到的帧训练一个背景字典。利用训练好的背景字典,我们将每一帧分离为背景和运动(前景),并将压缩后的运动与背景字典对应的背景重建系数一起存储。解码在一个反向过程中对编码帧进行。在广泛的监控视频上的实验结果表明,我们提出的方法显着减少了视频的大小,同时与最先进的编解码器相比,获得了更高的PSNR。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Motion matters: a novel framework for compressing surveillance videos
Currently, video surveillance plays a very important role in the fields of public safety and security. For storing the videos that usually contain extremely long sequences, it requires huge space. Video compression techniques can be used to release the storage load to some extent, such as H.264/AVC. However, the existing codecs are not sufficiently effective and efficient for encoding surveillance videos as they do not specifically consider the characteristic of surveillance videos, i.e. the background of surveillance video has intensive redundancy. This paper introduces a novel framework for compressing such videos. We first train a background dictionary based on a small number of observed frames. With the trained background dictionary, we then separate every frame into the background and motion (foreground), and store the compressed motion together with the reconstruction coefficient of the background corresponding to the background dictionary. The decoding is carried out on the encoded frame in an inverse procedure. The experimental results on extensive surveillance videos demonstrate that our proposed method significantly reduces the size of videos while gains much higher PSNR compared to the state of the art codecs.
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