Performance Improvement of Lossy Image Compression Based on Polynomial Curve Fitting and Vector Quantization

Othman, Shaimaa; Mohamed, Amr E.; Abouali, A.H.; Nossair, Zaki B.

doi:10.1007/978-981-16-0882-7_25

Cited by 4 publications

(4 citation statements)

References 23 publications

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“…Othman et al [ 27 ] examined a novel effectual lossy image compression approach dependent upon the polynomial curve fitting approximation approach that signifies several pixels of the image with less amount of polynomial coefficients. The projected approach begins with changing the image to a 1D signal and it separates this 1D signal into segments of variable length.…”

Section: Related Workmentioning

confidence: 99%

Manta Ray Foraging Optimization with Vector Quantization Based Microarray Image Compression Technique

Alkhaldi

Alsedais

Halawani

et al. 2022

Computational Intelligence and Neuroscience

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DNA microarray technologies enable the analysis of the expression of numerous genes in an individual experiment and become an important approach in the field of medicine and biology for investing genetic function, regulation, and interaction. Microarray images can be investigated well for obtaining the contained genetic data. But is it undesirable to retain the genetic data and avoid the microarray images? Due to considerable attention to DNA microarray and several experiments being performed under distinct conditions, a massive quantity of data gets produced over the globe. In order to store and share the microarray images, effective storage and communication models are needed in a natural way. Vector quantization (VQ) is a commonly utilized tool for compressing images, which mainly aims to produce effective codebooks comprising a collection of codewords. Therefore, this paper presents a manta ray foraging optimization (MRFO) with Linde–Buzo–Gray (LBG) based microarray image compression (MRFOLBG-MIC) technique. The LBG model is commonly utilized to design local optimal codebooks to compress images. The construction of codebooks can be defined as a nondeterministic polynomial time (NP) hard problem and can be resolved by the MRFO algorithm. The codebooks produced from LBG-VQ are optimized using the MRFO algorithm to attain optimum optimal codebooks. When the codebooks are produced by the MRFOLBG-MIC algorithm, Deflate model can be applied to compress the index tables. The design of the MRFO algorithm with LBG and Deflate based index table compression demonstrate the novelty of the work. For demonstrating the enhanced compression efficacy of the MRFOLBG-MIC model, a wide-ranging experimental validation process is performed using a benchmark dataset. The experimental outcomes inferred that the MRFOLBG-MIC model accomplished superior outcomes over the other existing models.

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Section: Related Workmentioning

confidence: 99%

Manta Ray Foraging Optimization with Vector Quantization Based Microarray Image Compression Technique

Alkhaldi

Alsedais

Halawani

et al. 2022

Computational Intelligence and Neuroscience

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“…The codebook is updated using the sine and cosine relationships. Figure (7) shows the flowchart of the proposed method for image compression.…”

Section: Figure (6): Codebook Coding As Members Of Isca Algorithmsmentioning

confidence: 99%

“…Production of codebook is a vital factor in VQ compression that directly influences the computational cost and the reconstructed image's quality. The Linde-Buzo-Gray (LBG) algorithm is a sophisticated VQ compression technique which uses the k-mean clustering algorithm to order to design codebooks [7]. A challenge for optimal compression is to search for the optimal codebook.…”

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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“…Vector quantization (VQ) has been a non-transformed compression system, is an efficient and effective mechanism for lossy IC [9]. The primary objective of VQ is to develop an effective codebook that comprises a collection of codeword where input image vector has been allocated according to the minimal Euclidean distance.…”

Section: Introductionmentioning

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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Performance Improvement of Lossy Image Compression Based on Polynomial Curve Fitting and Vector Quantization

Cited by 4 publications

References 23 publications

Manta Ray Foraging Optimization with Vector Quantization Based Microarray Image Compression Technique

Manta Ray Foraging Optimization with Vector Quantization Based Microarray Image Compression Technique

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

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