Abstract
An adaptive rate Compressive Sensing (CS) method for video signals is proposed. The Blocked Compressive Sensing (BCS) scheme is adopted in this method. Firstly, each video frame is blocked and measured by the BCS scheme, and then the mean and variance of each image block are estimated by observing the CS measurement results. Using the mean and variance of each image block, the sparsity of the block is estimated and then the block can be classified. Adaptive rate sampling is realized by assigning different sampling rates to different classes. At the same time, in order to make better use of the correlation between video frames, a reference block subtraction method is also designed in this paper, which uses the estimates of the sparsity of image blocks as the basis for the reference block update. All operations of the proposed method only depend on the CS measurement results of image blocks and all calculations are simple. Thus, the proposed method is suitable for implementation in CS sampling devices with limited computational performance. Experiment results show that, compared with the actual values, the sparsity estimates and block classification results of the proposed method are accurate. Compared with the latest adaptive Compressive Video Sensing methods, the reconstructed image quality of the proposed method is better.
Highlights
Compared with the traditional Compressive Sensing (CS) [1,2,3], the adaptive CS can adapt to the changes of the signal more effectively and achieve more reasonable signal sampling by using an appropriate CS matrix, sparse basis, sparse dictionary or sampling rates, to reduce the overall sampling rates and improve the quality of reconstructed image
In [26], researchers proposed a block compressive sensing scheme (BCS) method for video signal, which judges the complexity of the original signal through the change of the CS domain signal in the spatial and temporal dimensions, and adjusts the sampling rate of each image block in real-time to realize adaptive rate CS
Through the above experiment results, we can get the conclusion that the proposed method can accurately classify the image blocks only according to the known CS domain method can accurately classify the image blocks only according to the known CS domain signal, and the classification results are in good agreement with the actual sparsity of signal, and the classification results are in good agreement with the actual sparsity of the the signal
Summary
Compared with the traditional Compressive Sensing (CS) [1,2,3], the adaptive CS can adapt to the changes of the signal more effectively and achieve more reasonable signal sampling by using an appropriate CS matrix, sparse basis, sparse dictionary or sampling rates, to reduce the overall sampling rates and improve the quality of reconstructed image. In [26], researchers proposed a BCS method for video signal, which judges the complexity of the original signal through the change of the CS domain signal in the spatial and temporal dimensions, and adjusts the sampling rate of each image block in real-time to realize adaptive rate CS. The total energy of the foreground signal is estimated by using the CS domain signal, and the number of large value points of the original signal are estimated These methods avoid the dependence on signal reconstruction, but there are still some problems in these methods, such as the dependence on some specially designed sampling matrix, inaccurate rate estimation, only suitable for simple surveillance video signals etc. A new adaptive rate CS method based on the BCS scheme is proposed which only uses the CS domain signal and some simple operations to realize adaptive rate sampling and achieves better reconstruction performance. M1 ≤ B2 , this means that the signal is compressed into a low dimensional space
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