Adaptive compressive sensing of images using error between blocks

Li, Ran; Duan, Xiaomeng; Lv, Yue

doi:10.1177/1550147718781751

Cited by 16 publications

(5 citation statements)

References 22 publications

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“…The authors of [10] proposed an ABCS algorithm that used the error between blocks. In this algorithm, the image is divided into equal-sized smaller blocks, and the error between each block and its adjacent blocks is then determined.…”

Section: Bcs Was Pioneered By Ganmentioning

confidence: 99%

“…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%

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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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Section: Bcs Was Pioneered By Ganmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Heng

Aimtongkham

et al. 2020

Sensors

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“…In reference [17] the standard deviation is used to allocate adaptive sampling rates to each block based on its own data structure, in addition to a fixed sampling rate, to achieve real sampling rate allocation. In this ABCS coding system investigated by Li et al [18], the structural complexity of blocks can be determined by the error between blocks and sampling rates are allotted according to the error values obtained. The ABCS method based on spatial entropy proposed in reference [19] has similar complexity and reconstruction quality like ABCS method based on error between blocks but differs only in the calculation process.…”

Section: Introductionmentioning

confidence: 99%

Full-Process Adaptive Encoding and Decoding Framework for Remote Sensing Images Based on Compression Sensing

Hu,

Liu,

Liu

et al. 2024

Remote Sensing

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Faced with the problem of incompatibility between traditional information acquisition mode and spaceborne earth observation tasks, starting from the general mathematical model of compressed sensing, a theoretical model of block compressed sensing was established, and a full-process adaptive coding and decoding compressed sensing framework for remote sensing images was proposed, which includes five parts: mode selection, feature factor extraction, adaptive shape segmentation, adaptive sampling rate allocation and image reconstruction. Unlike previous semi-adaptive or local adaptive methods, the advantages of the adaptive encoding and decoding method proposed in this paper are mainly reflected in four aspects: (1) Ability to select encoding modes based on image content, and maximizing the use of the richness of the image to select appropriate sampling methods; (2) Capable of utilizing image texture details for adaptive segmentation, effectively separating complex and smooth regions; (3) Being able to detect the sparsity of encoding blocks and adaptively allocate sampling rates to fully explore the compressibility of images; (4) The reconstruction matrix can be adaptively selected based on the size of the encoding block to alleviate block artifacts caused by non-stationary characteristics of the image. Experimental results show that the method proposed in this article has good stability for remote sensing images with complex edge textures, with the peak signal-to-noise ratio and structural similarity remaining above 35 dB and 0.8. Moreover, especially for ocean images with relatively simple image content, when the sampling rate is 0.26, the peak signal-to-noise ratio reaches 50.8 dB, and the structural similarity is 0.99. In addition, the recovered images have the smallest BRISQUE value, with better clarity and less distortion. In the subjective aspect, the reconstructed image has clear edge details and good reconstruction effect, while the block effect is effectively suppressed. The framework designed in this paper is superior to similar algorithms in both subjective visual and objective evaluation indexes, which is of great significance for alleviating the incompatibility between traditional information acquisition methods and satellite-borne earth observation missions.

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“…Zhang et al [19] used the standard deviations of blocks as the allocation factor. Many other image features are also effective for ABCS, such as the spatial entropy of blocks [20], the error between blocks [21], the block-based gradient field [11], the block boundary variation [12], and the statistical texture distinctiveness [9]. Moreover, some researchers combined multiple features to allocate the sampling rate of each block.…”

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%

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 compressive sensing of images using error between blocks

Cited by 16 publications

References 22 publications

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

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

Full-Process Adaptive Encoding and Decoding Framework for Remote Sensing Images Based on Compression Sensing

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

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