scholarly journals A General Rate-Distortion Optimization Method for Block Compressed Sensing of Images

Entropy ◽  
2021 ◽  
Vol 23 (10) ◽  
pp. 1354
Author(s):  
Qunlin Chen ◽  
Derong Chen ◽  
Jiulu Gong
Keyword(s):  
Compressed Sensing ◽  
Sampling Rate ◽  
Rate Distortion ◽  
Optimization Method ◽  
Rate Model ◽  
Bit Rate ◽  
Unified Framework ◽  
Uniform Quantization ◽  
Block Compressed Sensing ◽  
Predictive Quantization

Block compressed sensing (BCS) is a promising technology for image sampling and compression for resource-constrained applications, but it needs to balance the sampling rate and quantization bit-depth for a bit-rate constraint. In this paper, we summarize the commonly used CS quantization frameworks into a unified framework, and a new bit-rate model and a model of the optimal bit-depth are proposed for the unified CS framework. The proposed bit-rate model reveals the relationship between the bit-rate, sampling rate, and bit-depth based on the information entropy of generalized Gaussian distribution. The optimal bit-depth model can predict the optimal bit-depth of CS measurements at a given bit-rate. Then, we propose a general algorithm for choosing sampling rate and bit-depth based on the proposed models. Experimental results show that the proposed algorithm achieves near-optimal rate-distortion performance for the uniform quantization framework and predictive quantization framework in BCS.

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

Entropy ◽  
2020 ◽  
Vol 22 (1) ◽  
pp. 125
Author(s):  
Qunlin Chen ◽  
Derong Chen ◽  
Jiulu Gong ◽  
Jie Ruan
Keyword(s):  
Compressed Sensing ◽  
Signal To Noise Ratio ◽  
Sampling Rate ◽  
Rate Distortion ◽  
Image Transmission ◽  
Low Complexity ◽  
Rate Model ◽  
Bit Rate ◽  
Compression Performance ◽  
The Relationship

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%.

scholarly journals Block Compressed Sensing of Images Using Adaptive Granular Reconstruction

2016 ◽  
Vol 2016 ◽  
pp. 1-9
Author(s):  
Ran Li ◽  
Hongbing Liu ◽  
Yu Zeng ◽  
Yanling Li
Keyword(s):  
Computational Complexity ◽  
Compressed Sensing ◽  
Structural Characteristic ◽  
Rate Distortion ◽  
Reconstructed Image ◽  
Hard Thresholding ◽  
Low Computational Complexity ◽  
Principle Components Analysis ◽  
Reconstruction Performance ◽  
Block Compressed Sensing

In the framework of block Compressed Sensing (CS), the reconstruction algorithm based on the Smoothed Projected Landweber (SPL) iteration can achieve the better rate-distortion performance with a low computational complexity, especially for using the Principle Components Analysis (PCA) to perform the adaptive hard-thresholding shrinkage. However, during learning the PCA matrix, it affects the reconstruction performance of Landweber iteration to neglect the stationary local structural characteristic of image. To solve the above problem, this paper firstly uses the Granular Computing (GrC) to decompose an image into several granules depending on the structural features of patches. Then, we perform the PCA to learn the sparse representation basis corresponding to each granule. Finally, the hard-thresholding shrinkage is employed to remove the noises in patches. The patches in granule have the stationary local structural characteristic, so that our method can effectively improve the performance of hard-thresholding shrinkage. Experimental results indicate that the reconstructed image by the proposed algorithm has better objective quality when compared with several traditional ones. The edge and texture details in the reconstructed image are better preserved, which guarantees the better visual quality. Besides, our method has still a low computational complexity of reconstruction.

scholarly journals Adaptive Multiscale Block Compressed Sensing of Images based on Gray Level Co- Occurrence Matrix

2020 ◽  
Vol 13 (5) ◽  
pp. 169-175
Author(s):  
Jinfeng Li ◽  
◽  
Jinnan Guo ◽  
Shun Cao ◽  
Yutong Zhao
Keyword(s):  
Compressed Sensing ◽  
High Frequency ◽  
Sampling Rate ◽  
Texture Features ◽  
Gray Level ◽  
Limited Sampling ◽  
Block Compressed Sensing ◽  
Frequency Components ◽  
Image Edges ◽  
Occurrence Matrix

In conventional block compressed sensing (BCS), the images are divided into small fixed-size blocks sampled at the same sub-rate. The sparsities and high-frequency components of the images are ignored, and the reconstruction qualities of the complex texture images are poor. An adaptive multiscale variant of the block compressed sensing was proposed to reconstruct the texture details of the images. The texture features of the images were obtained from the high-frequency components by the three-level wavelet transform and analyzed on the basis of the gray level co-occurrence matrix. A mathematical model was established to adjust the block sizes of the images automatically and allocate the limited sampling resource adaptively. The smoothed projected Landweber (SPL) was utilized to reconstruct the images. The accuracy of the proposed algorithm was verified by the simulation experiments. Results demonstrate that the texture details of the reconstructed images are abundant. The image edges are also clear, and the blocking artifacts are effectively eliminated. The reconstruction qualities of images, especially the partial images, are considerably improved at different sub-sampling rates. The proposed algorithm achieves a 2.42–3.3 dB gain in reconstruction PSNR for the Barbara image over the original BCS-SPL at a sub-sampling rate of 0.3. No remarkable differences are noted between the reconstructed and original texture blocks in visual sensation. The proposed algorithm provides evidence for the compression and reconstruction of the images with complex texture details.

scholarly journals Image Encryption Scheme Based on Multiscale Block Compressed Sensing and Markov Model

Entropy ◽  
2021 ◽  
Vol 23 (10) ◽  
pp. 1297
Author(s):  
Yuandi Shi ◽  
Yinan Hu ◽  
Bin Wang
Keyword(s):  
Compressed Sensing ◽  
Markov Model ◽  
Image Encryption ◽  
Sampling Rate ◽  
Coefficient Matrix ◽  
Poor Quality ◽  
Encryption Scheme ◽  
Block Compressed Sensing ◽  
The Difference

Many image encryption schemes based on compressed sensing have the problem of poor quality of decrypted images. To deal with this problem, this paper develops an image encryption scheme by multiscale block compressed sensing. The image is decomposed by a three-level wavelet transform, and the sampling rates of coefficient matrices at all levels are calculated according to multiscale block compressed sensing theory and the given compression ratio. The first round of permutation is performed on the internal elements of the coefficient matrices at all levels. Then the coefficient matrix is compressed and combined. The second round of permutation is performed on the combined matrix based on the state transition matrix. Independent diffusion and forward-backward diffusion between pixels are used to obtain the final cipher image. Different sampling rates are set by considering the difference of information between an image’s low- and high-frequency parts. Therefore, the reconstruction quality of the decrypted image is better than that of other schemes, which set one sampling rate on an entire image. The proposed scheme takes full advantage of the randomness of the Markov model and shows an excellent encryption effect to resist various attacks.

Ultrasonic Block Compressed Sensing Imaging Reconstruction Algorithm Based on Wavelet Sparse Representation

2020 ◽  
Vol 16 (3) ◽  
pp. 262-272
Author(s):  
Guangzhi Dai ◽  
Zhiyong He ◽  
Hongwei Sun
Keyword(s):  
Compressed Sensing ◽  
Sparse Representation ◽  
Linear Array ◽  
Imaging System ◽  
Ultrasonic Imaging ◽  
Large Data ◽  
Reconstruction Algorithm ◽  
Array Transducer ◽  
Total Data ◽  
Block Compressed Sensing

Background: This study is carried out targeting the problem of slow response time and performance degradation of imaging system caused by large data of medical ultrasonic imaging. In view of the advantages of CS, it is applied to medical ultrasonic imaging to solve the above problems. Objective: Under the condition of satisfying the speed of ultrasound imaging, the quality of imaging can be further improved to provide the basis for accurate medical diagnosis. Methods: According to CS theory and the characteristics of the array ultrasonic imaging system, block compressed sensing ultrasonic imaging algorithm is proposed based on wavelet sparse representation. Results: Three kinds of observation matrices have been designed on the basis of the proposed algorithm, which can be selected to reduce the number of the linear array channels and the complexity of the ultrasonic imaging system to some extent. Conclusion: The corresponding simulation program is designed, and the result shows that this algorithm can greatly reduce the total data amount required by imaging and the number of data channels required for linear array transducer to receive data. The imaging effect has been greatly improved compared with that of the spatial frequency domain sparse algorithm.

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