Evolutionary multiobjective multiple description wavelet based image coding in the presence of mixed noise in images

Kusetoğulları, Hüseyin; Yavariabdi, Amir

doi:10.1016/j.asoc.2018.10.015

Cited by 5 publications

(4 citation statements)

References 53 publications

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“…Several image processing problems can be solved in a multiobjective context, for example the multi-objective image coding [25], the multi-objective change detection in multispectral images [26], multi-objective image segmentation [27,28].…”

Section: Multi-objective Optimization Problemmentioning

confidence: 99%

A multi-objective approach for designing optimized operation sequence on binary image processing

Lezcano

Noguera

Pinto-Roa

et al. 2020

Heliyon

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In binary image segmentation, the choice of the order of the operation sequence may yield to suboptimal results. In this work, we propose to tackle the associated optimization problem via multi-objective approach. Given the original image, in combination with a list of morphological, logical and stacking operations, the goal is to obtain the ideal output at the lowest computational cost. We compared the performance of two Multi-objective Evolutionary Algorithms (MOEAs): the Non-dominated Sorting Genetic Algorithm (NSGA-II) and the Strength Pareto Evolutionary Algorithm 2 (SPEA2). NSGA-II has better results in most cases, but the difference does not reach statistical significance. The results show that the similarity measure and the computational cost are objective functions in conflict, while the number of operations available and type of input images impact on the quality of Pareto set.

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Section: Multi-objective Optimization Problemmentioning

confidence: 99%

A multi-objective approach for designing optimized operation sequence on binary image processing

Lezcano

Noguera

Pinto-Roa

et al. 2020

Heliyon

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“…This strategy capitalizes on the computational advantages of FFT to accelerate the CS process while maintaining reconstruction fidelity. While many approaches can improve compression efficiency at the encoding stage [25][26][27][28], they are not necessarily applicable or effective for QCS.…”

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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“…In order to make the intelligence closer to users and better serve people, edge intelligence emerged at the historic moment, that is, combining intelligent technology and edge computing technology, pushing intelligent services from cloud computing centers to edge devices to improve the quality of intelligent services [3], [4]. Although data storage technology has been developing, the performance of computers has been increasing, and the channel transmission bandwidth has been continuously widened, but it still cannot keep up with people's requirements for data compression to reduce physical storage space [5], [6]. More and more people have higher requirements for changing demand for channel utilization.…”

Section: Introductionmentioning

confidence: 99%

Multimedia Image Compression Method Based on Biorthogonal Wavelet and Edge Intelligent Analysis

Liu

2020

IEEE Access

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At present, network image communication is still restricted by channel coding, image and multimedia transmission and other key technologies. Therefore, the transmission process needs to convert the image signal into a digital signal, and then use the relevant band compression technology to reduce these signals to narrow the occupied frequency band, that is, to reduce the amount of information to be transmitted in synchronous transmission. Wavelet transform is a powerful tool for image compression because of its low entropy, multi-resolution, decorrelation and flexible base selection. In this paper, a method of multimedia image compression based on biorthogonal wavelet packet is proposed, which includes the establishment of linear phase biorthogonal wavelet basis, the selection of 3 or 4 levels of wavelet decomposition and reconstruction stage, and the combination of improved band division and preservation strategy. Finally, a compression test is performed based on the selected wavelet basis function and the optimal decomposition and reconstruction layers of the standard test image, which enables to obtain a more ideal compression ratio. INDEX TERMS Multimedia image compression, biorthogonal wavelet, multimedia transmission, discrete cosine transform.

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Evolutionary multiobjective multiple description wavelet based image coding in the presence of mixed noise in images

Cited by 5 publications

References 53 publications

A multi-objective approach for designing optimized operation sequence on binary image processing

A multi-objective approach for designing optimized operation sequence on binary image processing

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

Multimedia Image Compression Method Based on Biorthogonal Wavelet and Edge Intelligent Analysis

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