HHO-Based Vector Quantization Technique for Biomedical Image Compression in Cloud Computing

Kumar, T. Satish; Jothilakshmi, S.; James, Batholomew Chibuike; Mohan, Prakash; Arulkumar, N.; Rekha, C.

doi:10.1142/s0219467822400083

Cited by 11 publications

(5 citation statements)

References 15 publications

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“…The most significant image compression standard is JPEG and its quantization is classified into two kinds such as vector quantization (VQ) and scalar quantization (SQ). It is an inconvertible compression technique and is widely utilized in the compressing of images, which includes some loss of information [ 7 ]. The main motive of VQ is producing an optimum codeword (CW) which comprises a collection of CWs, where else an input image vector is allotted on the basis of minimal Euclidean distance.…”

Section: Introductionmentioning

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: Introductionmentioning

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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“…Experiments showed that the proposed method was more successful than other meta-processing methods in maintaining the quality of the reconstructed images. In the diagram in Figure (9), the PSNR index is compared with the average bitrate compression methods on average. According to the comparisons made, the PSNR index in the proposed method is 27.24.…”

Section: Comparison and Discussionmentioning

confidence: 99%

“…Image compression is important because it reduces storage space and speeds up image transmission over the network. 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.…”

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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“…An effective method for compressing biomedical images using a CC platform and HHO-based LBG techniques was proposed by Satish Kumar et al [13]. The method that is provided allows for a seamless transition between the exploitation phase and the exploration phase.…”

Section: Related Workmentioning

confidence: 99%

Metaheuristics with Vector Quantization Enabled Codebook Compression Model for Secure Industrial Embedded Environment

Kumar¹,

Srinivasan²

2023

Intelligent Automation &Amp; Soft Computing

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At the present time, the Industrial Internet of Things (IIoT) has swiftly evolved and emerged, and picture data that is collected by terminal devices or IoT nodes are tied to the user's private data. The use of image sensors as an automation tool for the IIoT is increasingly becoming more common. Due to the fact that this organisation transfers an enormous number of photographs at any one time, one of the most significant issues that it has is reducing the total quantity of data that is sent and, as a result, the available bandwidth, without compromising the image quality. Image compression in the sensor, on the other hand, expedites the transfer of data while simultaneously reducing bandwidth use. The traditional method of protecting sensitive data is rendered less effective in an environment dominated by IoT owing to the involvement of third parties. The image encryption model provides a safe and adaptable method to protect the confidentiality of picture transformation and storage inside an IIoT system. This helps to ensure that image datasets are kept safe. The Linde-Buzo-Gray (LBG) methodology is an example of a vector quantization algorithm that is extensively used and a relatively new form of picture reduction known as vector quantization (VQ). As a result, the purpose of this research is to create an artificial humming bird optimization approach that combines LBG-enabled codebook creation and encryption (AHBO-LBGCCE) for use in an IIoT setting. In the beginning, the AHBO-LBGCCE method used the LBG model in conjunction with the AHBO algorithm in order to construct the VQ. The Burrows-Wheeler Transform (BWT) model is used in order to accomplish codebook compression. In addition, the Blowfish algorithm is used in order to carry out the encryption procedure so that security may be attained. A comprehensive experimental investigation is carried out in order to verify the effectiveness of the proposed algorithm in comparison to other algorithms. The experimental values ensure that the suggested approach and the outcomes are examined in a variety of different perspectives in order to further enhance them.

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HHO-Based Vector Quantization Technique for Biomedical Image Compression in Cloud Computing

Cited by 11 publications

References 15 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)

Metaheuristics with Vector Quantization Enabled Codebook Compression Model for Secure Industrial Embedded Environment

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