Entropy-assisted adaptive compressive sensing for energy-efficient visual sensors

Li, Ran; Duan, Xiaomeng; He, Wei; You, Lei

doi:10.1007/s11042-020-08900-y

Cited by 5 publications

(6 citation statements)

References 33 publications

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“…e visual sensor refers to the sensor that calculates the feature quantity of the object by performing image processing on the image taken by the camera and outputs the data and the judgment result [17]. e vision sensor is the direct source of information for the entire machine vision system, which is mainly composed of one or two graphic sensors, sometimes with a light projector and auxiliary equipment [18][19][20]. e main function of the vision sensor is to obtain enough original images to be processed by the machine vision system.…”

Section: Visual Sensing In Movie and Tv Animationmentioning

confidence: 99%

[Retracted] Film and Television Animation Sensing and Visual Image by Computer Digital Image Technology

Lian

Tong

2022

Journal of Mathematics

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In order to study the application of computer digital image processing technology in film and television (FAT) animation visual sensing expression, by studying the principle of digital image processing technology and visual sensing technology, a spatial image adaptive steganography image enhancement algorithm by multiscale filters is proposed to carry out enhancement processing of the original image in FAT production. This algorithm can provide more high-quality and refined original materials for FAT animation production, which is convenient for FAT animation postproduction to produce higher-resolution and clear FAT works. Finally, the algorithm is verified. The results show that the spatial image adaptive steganography image enhancement algorithm has high security, and the highest average detection error rate is 25.06%. When α = 0.4 , the security of the spatial image adaptive steganography image enhancement algorithm is up to 34.62% and the image distortion rate is low. The established image enhancement algorithm can significantly improve the security of the existing spatial image steganography algorithm under different embedding rates, especially at a high embedding rate; the improvement of the spatial domain steganography algorithm is greater. The proposed steganographic image enhancement algorithm by image preprocessing has higher security and better image enhancement effect.

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Section: Visual Sensing In Movie and Tv Animationmentioning

confidence: 99%

[Retracted] Film and Television Animation Sensing and Visual Image by Computer Digital Image Technology

Lian

Tong

2022

Journal of Mathematics

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“…The second step is to calculate the sampling rate or the number of measurements of each block according to the allocation method. The most commonly used non-iterative allocation method [7,8,[10][11][12][13][14][15][16]19] uses the linear proportion of the allocation factor to estimate the sampling rates. The sampling rate m i of the ith block can be expressed as follows:…”

Section: Complexity Analysis Of the Existing Abcs Schemesmentioning

confidence: 99%

“…The same measurement matrix measures the raster scan vector of each block. Since different blocks contain different amounts of valuable information, adaptive block compressed sensing (ABCS) methods [7][8][9][10][11][12][13][14][15][16] have been proposed to make full use of limited measurement resources.…”

Section: Introductionmentioning

confidence: 99%

“…Li et al [10] took the measurement contrast (MC) between the CS measurements of blocks as an allocation factor for allocating the block sampling rates. Li et al [13] designed an allocation factor called sensing entropy, which estimates the statistical information of block pixel values from CS measurements. However, most allocation factors have high computational complexity, which cannot guarantee the simplicity of the CS encoder.…”

Section: Introductionmentioning

confidence: 99%

“…Based on the allocation factors, there are usually iterative [13,14] and noniterative [7][8][9][10][11][12][17][18][19][20][21] methods used to allocate the sampling rate of each block. Since the non-iterative method has low complexity, it is suitable for the BCS encoder.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Low-Complexity Adaptive Sampling of Block Compressed Sensing Based on Distortion Minimization

Chen

Gong

2022

Sensors

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Block compressed sensing (BCS) is suitable for image sampling and compression in resource-constrained applications. Adaptive sampling methods can effectively improve the rate-distortion performance of BCS. However, adaptive sampling methods bring high computational complexity to the encoder, which loses the superiority of BCS. In this paper, we focus on improving the adaptive sampling performance at the cost of low computational complexity. Firstly, we analyze the additional computational complexity of the existing adaptive sampling methods for BCS. Secondly, the adaptive sampling problem of BCS is modeled as a distortion minimization problem. We present three distortion models to reveal the relationship between block sampling rate and block distortion and use a simple neural network to predict the model parameters from several measurements. Finally, a fast estimation method is proposed to allocate block sampling rates based on distortion minimization. The results demonstrate that the proposed estimation method of block sampling rates is effective. Two of the three proposed distortion models can make the proposed estimation method have better performance than the existing adaptive sampling methods of BCS. Compared with the calculation of BCS at the sampling rate of 0.1, the additional calculation of the proposed adaptive sampling method is less than 1.9%.

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Adaptive rate image compressive sensing based on the hybrid sparsity estimation model

Wang

Chen

2023

Digital Signal Processing

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Entropy-assisted adaptive compressive sensing for energy-efficient visual sensors

Cited by 5 publications

References 33 publications

[Retracted] Film and Television Animation Sensing and Visual Image by Computer Digital Image Technology

[Retracted] Film and Television Animation Sensing and Visual Image by Computer Digital Image Technology

Low-Complexity Adaptive Sampling of Block Compressed Sensing Based on Distortion Minimization

Adaptive rate image compressive sensing based on the hybrid sparsity estimation model

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