Adaptive Block-Based Compressed Video Sensing Based on Saliency Detection and Side Information

The setting of the measurement number for each block is very important for a block-based compressed sensing system. However, in practical applications, we only have the initial measurement results of the original signal on the sampling side instead of the original signal itself, therefore, we cannot directly allocate the appropriate measurement number for each block without the sparsity of the original signal. To solve this problem, we propose an adaptive block-based compressed video sensing scheme based on saliency detection and side information. According to the Johnson-Lindenstrauss lemma, we can use the initial measurement results to perform saliency detection and then obtain the saliency value for each block. Meanwhile, a side information frame which is an estimate of the current frame is generated on the reconstruction side by the proposed probability fusion model, and the significant coefficient proportion of each block is estimated through the side information frame. Both the saliency value and significant coefficient proportion can reflect the sparsity of the block. Finally, these two estimates of block sparsity are fused, so that we can simultaneously use intra-frame and inter-frame correlation for block sparsity estimation. Then the measurement number of each block can be allocated according to the fusion sparsity. Besides, we propose a global recovery model based on weighting, which can reduce the block effect of reconstructed frames. The experimental results show that, compared with existing schemes, the proposed scheme can achieve a significant improvement in peak signal-to-noise ratio (PSNR) at the same sampling rate.

Automated summary

The paper proposes an adaptive block-based compressed video sensing scheme based on saliency detection and side information, aiming to address the issue of allocating appropriate measurement numbers for each block without the sparsity of the original signal. By fusing saliency values and significant coefficient proportions to estimate block sparsity, and introducing a global recovery model to reduce block effects in reconstructed frames, the proposed scheme achieves a significant improvement in peak signal-to-noise ratio (PSNR) compared to existing schemes at the same sampling rate.