Saliency-based adaptive compressive sampling of images using measurement contrast

Li, Ran; He, Wei; Liu, Zhenghui; Li, Yanling; Fu, Zhangjie

doi:10.1007/s11042-017-4862-z

Cited by 16 publications

(20 citation statements)

References 17 publications

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“…Usually, a realtime ACS system adopts one-shot sampling. In addition, Li et al also wield compressive domain adaptive measurement, but they do not propose the principles of temporalspatial difference-based saliency in compressive domain [21]. At most, they utilise spatial difference-based saliency to some extent and temporal difference-based saliency is not considered, because their work is special for static image recovery, not for dynamic video recovery.…”

Section: Related-workmentioning

confidence: 99%

Compressive domain spatial–temporal difference saliency‐based realtime adaptive measurement method for video recovery

2019

IET Image Processing

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Section: Related-workmentioning

confidence: 99%

Compressive domain spatial–temporal difference saliency‐based realtime adaptive measurement method for video recovery

2019

IET Image Processing

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“…The experimental results show that the proposed method can effectively improve the quality of reconstructed images. In [ 11 ], researchers proposed a saliency-based adaptive CS scheme that allocates more measurements to salient blocks but fewer to non-salient blocks, which extracts the information of saliency by using the difference between CS measurement results. Thus, it avoids the need to obtain the original image in the imaging system.…”

Section: Introductionmentioning

confidence: 99%

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

Wang

Chen

2021

Entropy

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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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“…To mitigate this problem, compressed sensing (CS) was recently presented. Developed in 2004, CS has recently gained considerable attention from researchers because it can be used to compress multimedia data such as images and videos effectively [9][10][11][12][13][14][15][16][17]. Moreover, in the fields of data compression and communication, CS is one of the best theories due to its performance and nonadaptive coding, and its encoding and decoding operations are independent [18].…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, adaptive block compressed sensing (ABCS) algorithms have been proposed [9,14,16,21,22] to address the issues of BCS. These ABCS algorithms adaptively allocate the sampling to each block based on an image feature, i.e., either saliency, standard deviation, edge, or texture.…”

Section: Introductionmentioning

confidence: 99%

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Fuzzy Adaptive-Sampling Block Compressed Sensing for Wireless Multimedia Sensor Networks

Heng

Aimtongkham

et al. 2020

Sensors

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: The transmission of high-volume multimedia content (e.g., images) is challenging for a resource-constrained wireless multimedia sensor network (WMSN) due to energy consumption requirements. Redundant image information can be compressed using traditional compression techniques at the cost of considerable energy consumption. Fortunately, compressed sensing (CS) has been introduced as a low-complexity coding scheme for WMSNs. However, the storage and processing of CS-generated images and measurement matrices require substantial memory. Block compressed sensing (BCS) can mitigate this problem. Nevertheless, allocating a fixed sampling to all blocks is impractical since each block holds different information. Although solutions such as adaptive block compressed sensing (ABCS) exist, they lack robustness across various types of images. As a solution, we propose a holistic WMSN architecture for image transmission that performs well on diverse images by leveraging saliency and standard deviation features. A fuzzy logic system (FLS) is then used to determine the appropriate features when allocating the sampling, and each corresponding block is resized using CS. The combined FLS and BCS algorithms are implemented with smoothed projected Landweber (SPL) reconstruction to determine the convergence speed. The experiments confirm the promising performance of the proposed algorithm compared with that of conventional and state-of-the-art algorithms.

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Saliency-based adaptive compressive sampling of images using measurement contrast

Cited by 16 publications

References 17 publications

Compressive domain spatial–temporal difference saliency‐based realtime adaptive measurement method for video recovery

Compressive domain spatial–temporal difference saliency‐based realtime adaptive measurement method for video recovery

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

Fuzzy Adaptive-Sampling Block Compressed Sensing for Wireless Multimedia Sensor Networks

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