Low-Complexity Vector Quantized Compressed Sensing via Deep Neural Networks

Leinonen, Markus; Codreanu, Marian

doi:10.1109/ojcoms.2020.3020131

Cited by 5 publications

(2 citation statements)

References 55 publications

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“…Subsequently, to further amplify the efficiency of the vector quantization technique, ref. [ 17 ] has leveraged deep neural networks. Due to the high computational complexity of vector quantization, scalar quantization is more suitable for CS measurements.…”

Section: Introductionmentioning

confidence: 99%

A Convolutional Neural Network-Based Quantization Method for Block Compressed Sensing of Images

Gong,

Chen,

Zhu

et al. 2024

Entropy

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Block compressed sensing (BCS) is a promising method for resource-constrained image/video coding applications. However, the quantization of BCS measurements has posed a challenge, leading to significant quantization errors and encoding redundancy. In this paper, we propose a quantization method for BCS measurements using convolutional neural networks (CNN). The quantization process maps measurements to quantized data that follow a uniform distribution based on the measurements’ distribution, which aims to maximize the amount of information carried by the quantized data. The dequantization process restores the quantized data to data that conform to the measurements’ distribution. The restored data are then modified by the correlation information of the measurements drawn from the quantized data, with the goal of minimizing the quantization errors. The proposed method uses CNNs to construct quantization and dequantization processes, and the networks are trained jointly. The distribution parameters of each block are used as side information, which is quantized with 1 bit by the same method. Extensive experiments on four public datasets showed that, compared with uniform quantization and entropy coding, the proposed method can improve the PSNR by an average of 0.48 dB without using entropy coding when the compression bit rate is 0.1 bpp.

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Section: Introductionmentioning

confidence: 99%

A Convolutional Neural Network-Based Quantization Method for Block Compressed Sensing of Images

Gong,

Chen,

Zhu

et al. 2024

Entropy

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show abstract

“…Over the past decade, a large number of methods for reconstructing CS images have been developed (Li et al, 2013;Metzler et al, 2016). These algorithms are based on solving an optimization problem by iterative algorithms (Jung et al, 2021;Leinonen & Codreanu, 2020a). However, the CS has problems such as the need to design a measurement matrix, the iterative and time-consuming reconstruction algorithms, as well as the need to find the basis in which the signal under consideration is sparse.…”

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confidence: 99%

RQCSNet: A deep learning approach to quantized compressed sensing of remote sensing images

Mirrashid

Shirazi

2021

Expert Systems

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In this article, a deep neural network is proposed that can be used to perform a quantized compressed sensing of remote sensing images. This network, dubbed RQCSNet consists of three parts: sensing, nonlinear quantization, and reconstruction. All network's parts are trained simultaneously according to the input data. RQCSNet has fully convolutional architecture, and it can work with different resolutions of the input images without the need for large initial fully connected layers. Furthermore, its structure reduces the blocky artefacts in the final reconstructed images by considering the effects of different image blocks in the reconstruction process. The proposed model has the ability to learn the nonlinear quantization function according to the input data and sending the measured values from the transmitter to the receiver with a fewer bit budget. Also, RQCSNet can learn the compressed sensing reconstruction algorithm without initial image reconstruction (that is usually the first step of reconstruction in other models), which reduces network flops. The results are presented on standard datasets of UCMerced as a remote sensing data that is the main concentration of this article, and as well as the set11 which is a dataset widely used for the benchmark of the state-of-the-art models. The experiments show that RQCSNet has a better performance than existing models in terms of the quality of image reconstruction and time complexity. As a benchmark, our network has a mean PSNR improvement of about 1.2 dB in reconstructed images compared to available techniques, at a measurement rate of 0.25. The effect of adding noise to the quantized measured data from the transmitter to the receiver at the time of transmission is also investigated in this article, and it is shown that the quality of image reconstruction will be robust to the noise once adding a noise layer to the network during training.

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A Parametric Lossy Compression Techniques for Biosignals: A Review

Dasan¹,

Gnanaraj²

2022

Wireless Pers Commun

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Low-Complexity Vector Quantized Compressed Sensing via Deep Neural Networks

Cited by 5 publications

References 55 publications

A Convolutional Neural Network-Based Quantization Method for Block Compressed Sensing of Images

A Convolutional Neural Network-Based Quantization Method for Block Compressed Sensing of Images

RQCSNet: A deep learning approach to quantized compressed sensing of remote sensing images

A Parametric Lossy Compression Techniques for Biosignals: A Review

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