A New Multistage Lattice Vector Quantization with Adaptive Subband Thresholding for Image Compression

Salleh, Mohd Khairul Mohd; Soraghan, John J.

doi:10.1155/2007/92928

Cited by 8 publications

(6 citation statements)

References 23 publications

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“…It was initiated and developed between the years 1960s to 1970s by a team called Holland team and is being used for many constrained and unconstrained optimization problems. It is inspired and developed by in-depth study of natural selection of Charles Darwin's theory [25]. GA being a nonswarm-based technique consists of chromosome for each and every population or solution of the problem.…”

Section: Genetic Algorithm (Ga)mentioning

confidence: 99%

“…It is inspired by the searching behavior of particles; some examples are swarm of fish or birds and was developed in the year 1995 by Eberhart and Kennedy 1995 [25]. The PSO, follows randomness and some intelligence in updation of the both particle positions and velocity.…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

“…A weighted membership function is altered by the spatial information of local and global to improve the results of thresholding MR images in conditional spatial FCM [23]. CT images are segmented by SVM using various kernel functions and optimization of sequential minimal using threshold optimization [24][25][26]. Kumari [29].…”

Section: Introductionmentioning

confidence: 99%

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Pipeline of Optimization Techniques for Multi-Level Thresholding in Medical Image Compression Using 2D Histogram

Gollu¹,

Sravani²,

Prakash³

et al. 2021

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In recent times, medical scan images are crucial for accurate diagnosis by medical professionals. Due to the increasing size of the medical images, transfer and storage of images require huge bandwidth and storage space, and hence needs compression. In this paper, multilevel thresholding using 2-D histogram is proposed for compressing the images. In the proposed work, hybridization of optimization techniques viz., Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Symbiotic Organisms Search (SOS) is used to optimize the multilevel thresholding process by assuming the Renyi entropy as an objective function. Meaningful clusters are possible with optimal threshold values, which lead to better image compression. For performance evaluation, the proposed work has been examined on six Magnetic Resonance (MR) images of brain and compared with individual optimization techniques as well as with 1-D histogram. Recent study reveals that peak signal to noise ratio (PSNR) fail in measuring the visual quality of reconstructed image because of mismatch with the objective mean opinion scores (MOS). So, we incorporate weighted PSNR (WPSNR) and visual PSNR (VPSNR) as performance measuring parameters of the proposed method. Experimental results reveal that hGAPSO-SOS method can be accurately and efficiently used in problem of multilevel thresholding for image compression.

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Section: Genetic Algorithm (Ga)mentioning

confidence: 99%

Section: Particle Swarm Optimizationmentioning

confidence: 99%

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Pipeline of Optimization Techniques for Multi-Level Thresholding in Medical Image Compression Using 2D Histogram

Gollu¹,

Sravani²,

Prakash³

et al. 2021

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show abstract

“…Image compression can also be performed with Multistage Lattice Vector Quantization (MLVQ) and by thresholding of Discrete Wavelet Transform (DWT) coefficients. Proposed combination tries to minimize the quantization error and its computational complexity is less compared to ordinary VQ (Salleh and Soraghan, 2007). Kaveh et al, proposed a 2-D discrete wavelet transform based image thresholding by optimal thresholding the DWT coefficients with Particle Swarm Optimization (PSO) for image compression.…”

Section: Related Work In Image Compres-sionmentioning

confidence: 99%

New Bacteria Foraging and Particle Swarm Hybrid Algorithm for Medical Image Compression

Kumari

Rao

2018

Image Anal Stereol

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For perfect diagnosis of brain tumour, it is necessary to identify tumour affected regions in the brain in MRI (Magnetic Resonance Imaging) images effectively and compression of these images for transmission over a communication channel at high speed with better visual quality to the experts. An attempt has been made in this paper for identifying tumour regions with optimal thresholds which are optimized with the proposed Hybrid Bacteria Foraging Optimization Algorithm (BFOA) and Particle Swarm Optimization (PSO) named (HBFOA-PSO) by maximizing the Renyi’s entropy and Kapur’s entropy. BFOA may be trapped into local optimal problem and delay in execution time (convergence time) because of random chemo taxis steps in the procedure of algorithm and to get global solution, a theory of swarming is commenced in the structure of HBFOA-PSO. Effectiveness of this HBFOA-PSO is evaluated on six different MRI images of brain with tumours and proved to be better in Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE) and Fitness Function.

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“…To this goal, several compression techniques are proposed. We can mention the scalar/vector quantification, differential coding, predictive coding and transformed coding (Salleh and Soraghan, 2007;Jolivet and Stouls, 1972;Pesquet and Tziritas, 1988;Trabuco et al, 2017). Among these techniques, those using transforms offer better results (Iqbal et al, 2007).…”

Section: Introductionmentioning

confidence: 99%

Use of Lazy Wavelet and DCT for Vibration Signal Compression

Okassa¹,

Ngantcha²,

Ndtoungou³

et al. 2021

American Journal of Engineering and Applied Sciences

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In this study, we compress and decompress the vibration signals from the functioning of a ball bearing. The methodology used to compress the vibration data in this study is to reduce the size of the data by reducing the spectral redundancy of the samples. We have used the DCT, which is recognized for its representational parsimony and bleaching power. To reduce the execution time of the algorithm, we used the Lazy wavelet. This wavelet separates the original signal into two signals half the size of the original signal. Parallel processing of two halves of the original signal reduces the computational load of the algorithm. We tested (compressed and then decompressed) these signals using three compression algorithms separately under the same quantification and coding conditions. These are the algorithms based on DCT, WHT and the Lazy Wavelet associated with DCT. The comparison made on the basis of the measurements of SNR, MFD, MSE, PRD and CR allowed to retain the algorithm based on the use of the Lazy wavelet and the discrete cosine transform. The results are considered very encouraging.

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A New Multistage Lattice Vector Quantization with Adaptive Subband Thresholding for Image Compression

Cited by 8 publications

References 23 publications

Pipeline of Optimization Techniques for Multi-Level Thresholding in Medical Image Compression Using 2D Histogram

Pipeline of Optimization Techniques for Multi-Level Thresholding in Medical Image Compression Using 2D Histogram

New Bacteria Foraging and Particle Swarm Hybrid Algorithm for Medical Image Compression

Use of Lazy Wavelet and DCT for Vibration Signal Compression

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