中文摘要：

线性动态系统的视频压缩感知（CS-LDS）是指从随机采样数据中直接估计出模型参数,然而对所有视频帧采取同样的采样方式,使得采样数据存在一定的时间冗余。针对这一问题,结合自适应压缩采样技术提出了一种自适应的改进算法。首先,对视频信号建立线性动态系统（LDS）模型;然后,通过自适应压缩采样方法得到视频信号的采样数据;最后,通过采样数据估计出系统模型参数,实现视频信号的重构。实验结果表明,在不影响视频重构质量的条件下,所提方法相对于CS-LDS算法,不仅能够节省统一测量过程中20%~40%的采样数据,而且平均每帧能够节省0.1~0.3 s的运行时间。改进后的算法降低了采样数目与算法运行时间。

英文摘要：

The model parameters of Video Compressed Sensing of Linear Dynamic System（ CS-LDS） can be estimated directly from random sampling data. If all video frames are sampled in the same way, the sampling data will be redundant. To solve this problem, an adaptive improvement algorithm based on adaptive compression sampling technology was proposed in this paper. Firstly, a Linear Dynamic System（ LDS） model of the video signal was established. And then the sampling data of video signal was obtained by using the adaptive compression sampling method. Finally, the model parameters were estimated and the video signal was reconstructed by the sampling data. Without affecting the video reconstruction quality, the experimental results show that the proposed algorithm is better than the CS-LDS algorithm, it can not only reduce 20%- 40%sampling data in the uniform measurement process, but also save the average running time of 0. 1- 0. 3 s per frame. The improved algorithm reduces the number of samples and the algorithm＇s running time.