Side information hybrid generation based on improved motion vector field

Wang, Wei; Li, Jingjian; Hong, Min-Cheol; Chen, Jianhua

doi:10.1007/s11042-021-10870-8

Cited by 5 publications

(3 citation statements)

References 19 publications

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“…Here, we use a PFM to generate high-quality SIF. As shown in Figure 2, we use the previously reconstructed frames to perform motion estimation to obtain the motion vectors (MV) of current SIF, and then we can use these MV for motion compensation to obtain the SIF [13].…”

Section: Side Information Generationmentioning

confidence: 99%

“…With the SIF, we can use them to calculate the proportion of the significant coefficients of each block. Considering that the SIF are generated on the reconstruction side, we need to use the feedback channel to transmit the obtained proportion of the significant coefficients to the sampling side, in which the feedback channel is a very common channel in the distributed video coding system [12,13]. In this paper, the feedback channel is used to feed back the obtained proportion of each block to the sampling side, that is, each block only needs to feed back a proportion value to the sampling side.…”

Section: Evaluation Of Side Information Generationmentioning

confidence: 99%

“…On the basis of the above consideration, we propose an adaptive block-based compressed video sensing (ABCVS) scheme to comprehensively estimate the measurement number of each block by using the saliency detection in the measurement domain and the side information [12,13] generated on the reconstruction side. Firstly, on the sampling side, the saliency value of each block of the current frame is estimated in the measurement domain.…”

Section: Introductionmentioning

confidence: 99%

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

Wang

Chen

2021

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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.

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Section: Side Information Generationmentioning

confidence: 99%

Section: Evaluation Of Side Information Generationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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