Vector Quantization Image Compression Algorithm Based on Bat Algorithm of Adaptive Separation Search

Guo, Juan; Wu, Chenhan; Huang, Zhiling; Wang, F. J.; Huang, M. T.

doi:10.1007/978-3-030-89701-7_16

Cited by 4 publications

(7 citation statements)

References 11 publications

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“…Increasing the SSIM index (X, Y) indicates better image quality. In the diagram of Figure (11), the SSIM index is compared between the methods, HHO-LBG, WOA-LBG, BA-LBG, FA-LBG, PSO-LBG in different Bitrates.…”

Section: Figure (9): Comparison Of Psnr Index Of the Proposed Methods...mentioning

confidence: 99%

“…From the combination of Equation ( 8) and Equation (11), Equation ( 12) is created, which combines the proposed modeling for search with simple sine and cosine modeling. In this equation, a can be proposed as equation ( 13):…”

Section: Improvement Of Sine and Cosine Algorithms (Isca)mentioning

confidence: 99%

“…Our motivation in this paper is to present an image compression version based on the LBG algorithm [9]. So far, optimization methods such as WOA [10], Bat algorithm [11], and Firefly algorithm [12] used to compress the images. In these studies, the behavior of living things was used to optimize image compression.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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: Figure (9): Comparison Of Psnr Index Of the Proposed Methods...mentioning

confidence: 99%

Section: Improvement Of Sine and Cosine Algorithms (Isca)mentioning

confidence: 99%

See 1 more Smart Citation

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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“…The presented method accomplishes a smooth transition amongst exploitation and exploration. Guo et al [14] designed a bat algorithm based adopted separation search mode to enhance the codebook design with the mean square error (MSE) as the adaptation values. The presented method employs pulse emission rate for switching the loudness and the search mode to define the searching range, however, it continued suffering from a deficiency of searching ability.…”

Section: Related Workmentioning

confidence: 99%

DNA Computing with Water Strider Based Vector Quantization for Data Storage Systems

Jovith¹,

Sree²,

Rao³

et al. 2023

Computers, Materials &Amp; Continua

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The exponential growth of data necessitates an effective data storage scheme, which helps to effectively manage the large quantity of data. To accomplish this, Deoxyribonucleic Acid (DNA) digital data storage process can be employed, which encodes and decodes binary data to and from synthesized strands of DNA. Vector quantization (VQ) is a commonly employed scheme for image compression and the optimal codebook generation is an effective process to reach maximum compression efficiency. This article introduces a new DNA Computing with Water Strider Algorithm based Vector Quantization (DNAC-WSAVQ) technique for Data Storage Systems. The proposed DNAC-WSAVQ technique enables encoding data using DNA computing and then compresses it for effective data storage. Besides, the DNAC-WSAVQ model initially performs DNA encoding on the input images to generate a binary encoded form. In addition, a Water Strider algorithm with Linde-Buzo-Gray (WSA-LBG) model is applied for the compression process and thereby storage area can be considerably minimized. In order to generate optimal codebook for LBG, the WSA is applied to it. The performance validation of the DNAC-WSAVQ model is carried out and the results are inspected under several measures. The comparative study highlighted the improved outcomes of the DNAC-WSAVQ model over the existing methods.

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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

Rahebi²

2023

Preprint

View full text Add to dashboard Cite

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

Vector Quantization Image Compression Algorithm Based on Bat Algorithm of Adaptive Separation Search

Cited by 4 publications

References 11 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)

DNA Computing with Water Strider Based Vector Quantization for Data Storage Systems

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

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