Adaptive sampling for compressed sensing based image compression

Zhu, Shuyuan; Zeng, Bing; Gabbouj, Moncef

doi:10.1016/j.jvcir.2015.03.006

Cited by 50 publications

(23 citation statements)

References 18 publications

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“…Many compressive sensing studies describe the constraints of the measurement budget, such as allocating sensing resources for regions of interest [11,12] and adaptive sampling for block compressive sensing [13,14]. However, real-valued CS measurements must be quantified in CS-based imaging systems, and there is a given bit-budget constraint rather than a measurement budget.…”

Section: Introductionmentioning

confidence: 99%

Low-Complexity Rate-Distortion Optimization of Sampling Rate and Bit-Depth for Compressed Sensing of Images

Chen

Gong

et al. 2020

Entropy

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Compressed sensing (CS) offers a framework for image acquisition, which has excellent potential in image sampling and compression applications due to the sub-Nyquist sampling rate and low complexity. In engineering practices, the resulting CS samples are quantized by finite bits for transmission. In circumstances where the bit budget for image transmission is constrained, knowing how to choose the sampling rate and the number of bits per measurement (bit-depth) is essential for the quality of CS reconstruction. In this paper, we first present a bit-rate model that considers the compression performance of CS, quantification, and entropy coder. The bit-rate model reveals the relationship between bit rate, sampling rate, and bit-depth. Then, we propose a relative peak signal-to-noise ratio (PSNR) model for evaluating distortion, which reveals the relationship between relative PSNR, sampling rate, and bit-depth. Finally, the optimal sampling rate and bit-depth are determined based on the rate-distortion (RD) criteria with the bit-rate model and the relative PSNR model. The experimental results show that the actual bit rate obtained by the optimized sampling rate and bit-depth is very close to the target bit rate. Compared with the traditional CS coding method with a fixed sampling rate, the proposed method provides better rate-distortion performance, and the additional calculation amount amounts to less than 1%.

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Section: Introductionmentioning

confidence: 99%

Low-Complexity Rate-Distortion Optimization of Sampling Rate and Bit-Depth for Compressed Sensing of Images

Chen

Gong

et al. 2020

Entropy

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“…The concept of Bayesian adaptive sensing was introduced in [4]. An iterative scheme to design the sequence of sensing vectors was proposed in [5], improved in [6] and [7], and applied to image compression in [8]. Theoretical limits on adaptive compressive sensing were found in [9].…”

Section: Introductionmentioning

confidence: 99%

Oversampled Adaptive Sensing

Müller¹,

Bereyhi²,

Mecklcnbraukcr

2018

2018 Information Theory and Applications Workshop (ITA)

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We develop a Bayesian framework for sensing which adapts the sensing time and/or basis functions to the instantaneous sensing quality measured in terms of the expected posterior mean-squared error. For sparse Gaussian sources a significant reduction in average sensing time and/or mean-squared error is achieved in comparison to nonadaptive sensing. For compression ratio 3, a sparse 10% Gaussian source and equal average sensing times, the proposed method gains about 2 dB over the performance bound of optimum compressive sensing, about 3 dB over non-adaptive 3-fold oversampled orthogonal sensing and about 6 to 7 dB to LASSO-based recovery schemes while enjoying polynomial time complexity.We utilize that in the presence of Gaussian noise the mean-squared error conditioned on the current observation is proportional to the derivative of the conditional mean estimate with respect to this observation.

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“…Scheme A adopts the same adaptive measurements allocation strategy as ABCS [8] and scheme B adopts the same adaptive measurements allocation strategy (i.e.,based on the number of the significant DCT coefficients of each block) as reference [9]. Scheme A and scheme B respectively use a pixel domain based metric and a transform domain based block metric for adaptive measurements allocation.…”

Section: Resultsmentioning

confidence: 99%

“…Currently, there are generally two kinds of block-based statistical metric used for adaptive measurements allocation: pixel domain based [7], [8] and transform domain based [9], [10]. Our scheme adopts the latter kind of block metric because it is easier for implementation and has a relatively better performance.…”

Section: Metric Of Block Perceptual Compressibility Level In Dct Domainmentioning

confidence: 99%

Adaptive Perceptual Block Compressive Sensing for Image Compression

Qiao

Zhang

2016

IEICE Trans. Inf. & Syst.

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SUMMARYBecause the perceptual compressive sensing framework can achieve a much better performance than the legacy compressive sensing framework, it is very promising for the compressive sensing based image compression system. In this paper, we propose an innovative adaptive perceptual block compressive sensing scheme. Firstly, a new block-based statistical metric which can more appropriately measure each block's sparsity and perceptual sensibility is devised. Then, the approximated theoretical minimum measurement number for each block is derived from the new block-based metric and used as weight for adaptive measurements allocation. The obtained experimental results show that our scheme can significantly enhance both objective and subjective performance of a perceptual compressive sensing framework. key words: perceptual compressive sensing, adaptive measurements allocation, discrete cosine transform, image compression

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Adaptive sampling for compressed sensing based image compression

Cited by 50 publications

References 18 publications

Low-Complexity Rate-Distortion Optimization of Sampling Rate and Bit-Depth for Compressed Sensing of Images

Low-Complexity Rate-Distortion Optimization of Sampling Rate and Bit-Depth for Compressed Sensing of Images

Oversampled Adaptive Sensing

Adaptive Perceptual Block Compressive Sensing for Image Compression

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