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SAR IMAGE COMPRESSION USING ADAPTIVE DIFFERENTIAL EVOLUTION AND PATTERN SEARCH BASED K-MEANS VECTOR QUANTIZATION
Dergi Makalesi

Dergi Makalesi
Open Access
Kaynak :
Directory of Open Access Journals (DOAJ)
Dergi:
Image Analysis and Stereology
Yazar(lar) :
Karri Chiranjeevi , Umaranjan Jena
Yıl :
2018
Karri Chiranjeevi
Yayıncı Web Sitesi
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Kaydet Alıntıla
A novel Vector Quantization (VQ) technique for encoding the Bi-orthogonal wavelet decomposed image using hybrid Adaptive Differential Evolution (ADE) and a Pattern Search optimization algorithm (hADEPS) is proposed. ADE is a modified version of Differential Evolution (DE) in which mutation operation is made adaptive based on the ascending/descending objective function or fitness value and tested on twelve numerical benchmark functions and the results are compared and proved better than Genetic Algorithm (GA), ordinary DE and FA. ADE is a global optimizer which explore the global search space and PS is local optimizer which exploit a local search space, so ADE is hybridized with PS. In the proposed VQ, in a codebook of codewords, 62.5% of codewords are assigned and optimized for the approximation coefficients and the remaining 37.5% are equally assigned to horizontal, vertical and diagonal coefficients. The superiority of proposed hybrid Adaptive Differential Evolution and Pattern Search (hADE-PS) optimized vector quantization over DE is demonstrated. The proposed technique is compared with DE based VQ and ADE based quantization and with standard LBG algorithm. Results show higher Peak Signal-to-Noise Ratio (PSNR) and Structural Similiraty Index Measure (SSIM) indicating better reconstruction.
DOI : 10.5566/ias.1611
Anahtar Kelimeler : differential evolution (ADE), image compression, Linde-Buzo-Gray (LBG), Pattern Search (PS), vector quantization
Cilt: 37
Sayı: 1
Sayfa: 35 - 54
Sınıflandırma :
2
Image compression based on vector quantization using cuckoo search optimization technique
Dergi Makalesi

Dergi Makalesi
Open Access
Kaynak :
Directory of Open Access Journals (DOAJ)
Dergi:
Ain Shams Engineering Journal
Yazar(lar) :
Karri Chiranjeevi , Uma Ranjan Jena
Yıl :
2018
Karri Chiranjeevi
Yayıncı Web Sitesi
Kaydet Alıntıla
Most common vector quantization (VQ) is Linde Buzo Gray (LBG), that designs a local optimal codebook for image compression. Recently firefly algorithm (FA), particle swarm optimization (PSO) and Honey bee mating optimization (HBMO) were designed which generate near global codebook, but search process follows Gaussian distribution function. FA experiences a problem when brighter fireflies are insignificant and PSO undergoes instability in convergence when particle velocity is very high. So, we proposed Cuckoo search (CS) metaheuristic optimization algorithm, that optimizes the LBG codebook by levy flight distribution function which follows the Mantegna’s algorithm instead of Gaussian distribution. Cuckoo search consumes 25% of convergence time for local and 75% of convergence time for global codebook, so it guarantees the global codebook with appropriate mutation probability and this behavior is the major merit of CS. Practically we observed that cuckoo search algorithm has high peak signal to noise ratio (PSNR) and better fitness value compared to LBG, PSO-LBG, Quantum PSO-LBG, HBMO-LBG and FA-LBG at the cost of high convergence time. Keywords: Cuckoo search (CS), Firefly algorithm (FA), Particle swarm optimization (PSO), Linde-Buzo-Gray (LBG), Vector quantization, Image compression
ISSN: 2090-4479
Cilt: 9
Sayı: 4
Sayfa: 1417 - 1431
Sınıflandırma :
3
Hybrid gravitational search and pattern search–based image thresholding by optimising Shannon and fuzzy entropy for image compression
Dergi Makalesi

Dergi Makalesi
Kaynak :
Taylor and Francis
Dergi:
International Journal of Image and Data Fusion
Yayıncı:
Informa UK Limited
Yazar(lar) :
Karri Chiranjeevi , Umaranjan Jena
Yıl :
2017
Karri Chiranjeevi
Yayıncı Web Sitesi
Tam Metin
Kaydet Alıntıla
Image compression is very significant process in image transmission at high data rate over a communication channel and to increase the storage capacity of storage device. Ordinary image thresholding is a class of clustering technique used for image compression because of its simplicity, robustness and accuracy but it is computationally expensive when extending for multilevel image thresholding. An attempt is made in this paper to reduce the computational time of multilevel image thresholding using hybrid gravitational search algorithm and pattern search (hGSA-PS) by optimising a criterion such as Shannon entropy or Fuzzy entropy for seeking appropriate threshold values. From literature, gravitational search algorithm (GSA) is designed to explore the global search space (exploitation), and pattern search (PS) is designed to exploit a local search space (exploration), so we hybridise the GSA and PS to achieve exploitation and exploration of search space by incorporating strengths and weakness of both, and results are compared with differential evolution, particle swarm optimisation and bat algorithm and proved better in standard deviation, peak signal-to-noise ratio (PSNR), weighted PSNR and reconstructed image quality. The performance of the proposed algorithm is found better with fuzzy entropy compared to Shannon entropy.
DOI : 10.1080/19479832.2017.1338760
Anahtar Kelimeler : Image compression, image thresholding, Shannon entropy, Fuzzy entropy, gravitational search, pattern search
ISSN: 1947-9832
Sayfa: 1-34
Sınıflandırma :
4
Fast vector quantization using a Bat algorithm for image compression
Dergi Makalesi

Dergi Makalesi
Open Access
Kaynak :
Directory of Open Access Journals (DOAJ)
Dergi:
Engineering Science and Technology, an International Journal
Yazar(lar) :
Chiranjeevi Karri , Umaranjan Jena
Yıl :
2016
Chiranjeevi Karri
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 Piri Tam Metin Bulucu
Kaydet Alıntıla
Linde–Buzo–Gray (LBG), a traditional method of vector quantization (VQ) generates a local optimal codebook which results in lower PSNR value. The performance of vector quantization (VQ) depends on the appropriate codebook, so researchers proposed optimization techniques for global codebook generation. Particle swarm optimization (PSO) and Firefly algorithm (FA) generate an efficient codebook, but undergoes instability in convergence when particle velocity is high and non-availability of brighter fireflies in the search space respectively. In this paper, we propose a new algorithm called BA-LBG which uses Bat Algorithm on initial solution of LBG. It produces an efficient codebook with less computational time and results very good PSNR due to its automatic zooming feature using adjustable pulse emission rate and loudness of bats. From the results, we observed that BA-LBG has high PSNR compared to LBG, PSO-LBG, Quantum PSO-LBG, HBMO-LBG and FA-LBG, and its average convergence speed is 1.841 times faster than HBMO-LBG and FA-LBG but no significance difference with PSO.
DOI : 10.1016/j.jestch.2015.11.003
Anahtar Kelimeler : Vector quantization, Lindeu2013Buzou2013Gray (LBG), Particle swarm optimization (PSO), Quantum particle swarm algorithm (QPSO), Honey bee mating optimization (HBMO), Firefly algorithm (FA), Bat algorithm (BA)
ISSN: 2215-0986
Cilt: 19
Sayı: 2
Sayfa: 769 - 781
Sınıflandırma :
5
Image compression using explored bat algorithm by Renyi 2-d histogram based on multilevel thresholding
Dergi Makalesi

Dergi Makalesi
Kaynak :
Springer Link
Dergi:
Evolutionary Intelligence
Yazar(lar) :
V. Manohar , G. Laxminarayana , T. Satya Savithri
Konu :
Artificial Intelligence, Cognitive Neuroscience, Computer Vision and Pattern Recognition, Mathematics (miscellaneous)
Yıl :
2019
Yayıncı Web Sitesi
Tam Metin
Kaydet Alıntıla
The main objective of the image compression is to extract meaningful clusters from a given image. A meaningful cluster is possible with perfect threshold values, which are optimized by assuming Renyi entropy as an objective function. Due to the equal distribution of energy over the entire 1-D histogram, it is computationally complex. In order to improve the visual quality of a reconstructed image, a 2-D histogram based multilevel thresholding is proposed to maximize the Renyi entropy using explored bat algorithm. Thus procured results are compared with other optimization techniques and these are incorporated. It is the first time, incorporating a weighted peak signal to noise ratio (WPSNR) and the visual PSNR (VPSNR) in the proposed method, because of the failure in measuring the visual quality of peak signal to noise ratio (PSNR). Experimental results are examined on a standard set of images, which are observed precisely and efficiently in the multilevel thresholding problem.
DOI : 10.1007/s12065-019-00313-7
Anahtar Kelimeler : Explored bat algorithm, Image compression, 2-D histogram, Thresholding, Objective function, Renyi entropy
ISSN: 1864-5909 1864-5917
Sayı: 1
Cilt: 14
Sayfa: 75-85
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