Oppositional Glowworm Swarm based Vector Quantization Technique for Image Compression in Fiber Optic Communication

Alapatt, Bosco Paul; Philip, Felix M; Jims, Anupama

doi:10.1109/access51619.2021.9563298

Cited by 4 publications

(2 citation statements)

References 14 publications

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“…In image compression, the compression level should be done optimally to minimize damage to the edges of the images. Some studies have used meta-heuristic algorithms to enhance compression's quality [8]. In these methods, the goal is to find the best codebook for optimizing the compression of the LBG algorithm.…”

Section: Introductionmentioning

confidence: 99%

Compression of images with a mathematical approach based on sine and cosine equations and vector quantization (VQ)

Ghadami

Rahebi

2023

Soft Comput

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Image compression is one of the most attractive and practical topics. Without image compression, the image size becomes too large for storage. The transmission of uncompressing images on computer networks slows down and network bandwidth is wasted. Various approaches to image compression have been proposed so far, one of which is vector quantization using mathematical concepts and image processing to compress images. The LBG algorithm is a practical algorithm for compressing images using vector quantization concepts. Most researchers have used metaheuristic and optimization algorithms with a modeling approach of swarm behavior of living things to improve the quality of the LBG compression algorithm. This study uses a meta-heuristic method based on sine and cosine algorithms (SCA) to improve the quality of the image compression algorithm. In the proposed mathematical modeling method, the SCA algorithm is improved using spiral equations. The improved SCA algorithm is then used to find the optimal codebook in the LBG compression algorithm. Finding a better codebook in the proposed method will increase the quality of the compression images. The proposed method implemented in MATLAB software and experiments showed that the PSNR index in the proposed method improve the ratio of the LBG algorithm by about 13.73%. Evaluations show that the PSNR index of compressed images in the proposed method is higher and better than PBM, CS-LBG, FA-LBG, BA-LBG, HBMO-LBG, QPSO-LBG, PSO-LBG. The result shows the proposed method (or ISCA-LBG) has less time complexity than HHO and WOA compression algorithms.

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Section: Introductionmentioning

confidence: 99%

Compression of images with a mathematical approach based on sine and cosine equations and vector quantization (VQ)

Ghadami

Rahebi

2023

Soft Comput

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“…But, in comparison to natural images, RSI has its individual characteristic features. Usually, they contain a massive amount of ground objects that results in a great amount of details, like outlines of small targets, geometric information, edge, and texture information [3]. Consequently, it is hard to attain a higher coding performance to RSI since the coefficient of higher frequency sub-bands are still larger afterward the DWT.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Satin Bowerbird Optimizer Based Compression Technique for Remote Sensing Images

Saravanan¹,

Jayanthi²,

Sakthi³

et al. 2022

Computers, Materials &Amp; Continua

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Due to latest advancements in the field of remote sensing, it becomes easier to acquire high quality images by the use of various satellites along with the sensing components. But the massive quantity of data poses a challenging issue to store and effectively transmit the remote sensing images. Therefore, image compression techniques can be utilized to process remote sensing images. In this aspect, vector quantization (VQ) can be employed for image compression and the widely applied VQ approach is Linde-Buzo-Gray (LBG) which creates a local optimum codebook for image construction. The process of constructing the codebook can be treated as the optimization issue and the metaheuristic algorithms can be utilized for resolving it. With this motivation, this article presents an intelligent satin bowerbird optimizer based compression technique (ISBO-CT) for remote sensing images. The goal of the ISBO-CT technique is to proficiently compress the remote sensing images by the effective design of codebook. Besides, the ISBO-CT technique makes use of satin bowerbird optimizer (SBO) with LBG approach is employed. The design of SBO algorithm for remote sensing image compression depicts the novelty of the work. To showcase the enhanced efficiency of ISBO-CT approach, an extensive range of simulations were applied and the outcomes reported the optimum performance of ISBO-CT technique related to the recent state of art image compression approaches.

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Compression of images with a mathematical approach based on sine and cosine equations and vector quantization (VQ)

Ghadami

Rahebi²

2023

Preprint

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Image compression is one of the most attractive and practical topics. Without image compression, the image size becomes too large for storage. The transmission of uncompressing images on computer networks slows down and network bandwidth is wasted. Various approaches to image compression have been proposed so far, one of which is vector quantization using mathematical concepts and image processing to compress images. The LBG algorithm is a practical algorithm for compressing images using vector quantization concepts. Most researchers have used meta-heuristic and optimization algorithms with a modeling approach of swarm behavior of living things to improve the quality of the LBG compression algorithm. This study uses a meta-heuristic method based on sine and cosine algorithms (SCA) to improve the quality of the image compression algorithm. In the proposed mathematical modeling method, the SCA algorithm is improved using spiral equations. The improved SCA algorithm is then used to find the optimal codebook in the LBG compression algorithm. Finding a better codebook in the proposed method will increase the quality of the compression images. The proposed method implemented in MATLAB software and experiments showed that the PSNR index in the proposed method improve the ratio of the LBG algorithm by about 13.73%. Evaluations show that the PSNR index of compressed images in the proposed method is higher and better than PBM, CS-LBG, FA-LBG, BA-LBG, HBMO-LBG, QPSO-LBG, PSO-LBG. The result shows the proposed method (or ISCA-LBG) has less time complexity than HHO and WOA compression algorithms.

show abstract

Oppositional Glowworm Swarm based Vector Quantization Technique for Image Compression in Fiber Optic Communication

Cited by 4 publications

References 14 publications

Compression of images with a mathematical approach based on sine and cosine equations and vector quantization (VQ)

Compression of images with a mathematical approach based on sine and cosine equations and vector quantization (VQ)

Intelligent Satin Bowerbird Optimizer Based Compression Technique for Remote Sensing Images

Compression of images with a mathematical approach based on sine and cosine equations and vector quantization (VQ)

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