Piri Keşif Aracı

Piri Tam Metin Bulucu Özet Ek Bilgiler

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 :

Bilgisayar Bilimi %100

Multimedya Bilgi Sistemleri %84

Image compression based on vector quantization using cuckoo search optimization technique

Dergi Makalesi Open Access

Kaynak 1:

Directory of Open Access Journals (DOAJ)

Kaynak 2:

Science Direct

Yayıncı :

Ain Shams Engineering Journal

Yazar(lar) :

Karri Chiranjeevi , Uma Ranjan Jena

Yıl :

2018

Karri Chiranjeevi

Yayıncı Web Siteleri

Özet Ek Bilgiler

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

Kaynak 1: http://www.sciencedirect.com/science/article/pii/S209044791630140X üzerinden görüntüle.
Kaynak 2: http://dx.doi.org/10.1016/j.asej.2016.09.009 üzerinden görüntüle.

ISSN: 2090-4479
Cilt: 9
Sayı: 4
Sayfa: 1417 - 1431

Sınıflandırma :

Bilgisayar Bilimi %100

Kontrol Yapıları ve Mikro Programlama %27

Multimedya Bilgi Sistemleri %20

Örüntü Tanıma ve Grafikler %20

Hybrid gravitational search and pattern search–based image thresholding by optimising Shannon and fuzzy entropy for image compression

Dergi Makalesi

Kaynak :

Taylor and Francis

Yayıncı :

International Journal of Image and Data Fusion

Yazar(lar) :

Karri Chiranjeevi , Umaranjan Jena

Yıl :

2017

Karri Chiranjeevi

Ek Bilgiler

DOI : 10.1080/19479832.2017.1338760

ISSN: 1947-9832
Sayfa: 1-34

Image Compression Using Shannon Entropy-Based Image Thresholding

e-Kitap

Kaynak :

Springer Link

Yayıncı :

Computational Intelligence in Data Mining

Yazar(lar) :

Karri Chiranjeevi , Uma Ranjan Jena , Asha Harika

Yıl :

2017

Karri Chiranjeevi

Özet Ek Bilgiler

In this paper, we proposed multilevel image thresholding for image compression using Shannon entropy which is maximized by the nature-inspired Bacterial Foraging Optimization Algorithm (BFOA). Ordinary threading methods are computationally expensive, while extending for multilevel image thresholding, so there is a need of optimization techniques to reduce the computational time. Particle swarm optimization undergoes instability when particle velocity is maximum. So we proposed a BFOA-based multilevel image thresholding by maximizing Shannon entropy and the results are compared with differential evolution and Particle swarm optimization and proved better in Peak signal-to-noise ratio (PSNR), Compression ratio and reconstructed image quality.

DOI : 10.1007/978-981-10-3874-7_10

Anahtar Kelimeler : Image compression, Image thresholding, Shannon entropy, Bacterial foraging optimization algorithm, Differential evolution

ISBN: 978-981-10-3874-7
Sayfa: 101-110

Sınıflandırma :

Bilgisayar Bilimi %100

Multimedya Bilgi Sistemleri %62

Hybrid Cuckoo Search Based Evolutionary Vector Quantization for Image Compression

e-Kitap

Kaynak :

Springer Link

Yayıncı :

Artificial Intelligence and Computer Vision

Yazar(lar) :

Karri Chiranjeevi , Umaranjan Jena , P. M. K. Prasad

Yıl :

2017

Karri Chiranjeevi

Özet Ek Bilgiler

Vector quantization (VQ) is the technique of image compression that aims to find the closest codebook by training test images. Linde Buzo and Gray (LBG) algorithm is the simplest technique of VQ but doesn’t guarantee optimum codebook. So, researchers are adapting the applications of optimization techniques for optimizing the codebook. Firefly and Cuckoo search (CS) generate a near global codebook, but undergoes problem when non-availability of brighter fireflies in search space and fixed tuning parameters for cuckoo search. Hence a Hybrid Cuckoo Search (HCS) algorithm is proposed that optimizes the LBG codebook with less convergence time by taking McCulloch’s algorithm based levy flight distribution function and variant of searching parameters (mutation probability and step of the walk). McCulloch’s algorithm helps the codebook in the direction of the global codebook. The variation in the parameters of HCS prevents the algorithm from being trapped in the local optimum. Performance of HCS was tested on four benchmark functions and compared with other metaheuristic algorithms. Practically, it is observed that the Hybrid Cuckoo Search algorithm has high peak signal to noise ratio and a fitness function compared to LBG, PSO-LBG, FA-LBG and CS-LBG. The convergence time of HCS-LBG is 1.115 times better to CS-LBG.

DOI : 10.1007/978-3-319-46245-5_7

Anahtar Kelimeler : Vector quantization, Linde-Buzo-Gray (LBG), Particle swarm optimization (PSO), Firefly algorithm (FA), Cuckoo search algorithm (CS), Hybrid cuckoo search algorithm (HCS)

ISBN: 978-3-319-46245-5
Sayfa: 89-114

Sınıflandırma :

Bilgisayar Bilimi %100

Algoritma Analizi %66

Notice of Removal: Comparative study of image compression

Konferans Makalesi

Kaynak :

IEEE Xplore

Yayıncı :

2015 International Conference on Electrical, Electronics, Signals, Communication and Optimization (EESCO)

Yazar(lar) :

Karri Chiranjeevi , U.R. Jena , Ch. Babji Prasad , Akula Trinadh

Konu :

Communication, Networking and Broadcast Technologies

Yıl :

2015

Karri Chiranjeevi

Özet Ek Bilgiler

Removed

DOI : 10.1109/EESCO.2015.7253947

Sayfa: 1-6
Konferans Tarihi: 24-25 Jan. 2015
Konferans Yeri: Visakhapatnam, India

Sınıflandırma :

Bilgisayar Bilimi %50

Multimedya Bilgi Sistemleri %26

Modified Firefly Algorithm (MFA) Based Vector Quantization for Image Compression

e-Kitap

Kaynak :

Springer Link

Yayıncı :

Computational Intelligence in Data Mining—Volume 2

Yazar(lar) :

Karri Chiranjeevi , Uma Ranjan Jena , B. Murali Krishna , Jeevan Kumar

Yıl :

2016

Karri Chiranjeevi

Özet Ek Bilgiler

Firefly algorithm optimization is based on the attractiveness/brightness of the firefly. In firefly algorithm, a lighter (lesser fitness function) firefly move towards the brighter firefly (higher fitness function) with amplitude proportional to Euclidean distance between the lighter and brighter firefly. If no such brighter firefly is found then it moves randomly is search space. This random move causes chance of decrement in brightness of the brighter firefly depending on the direction in which it is move. We proposed a modified firefly algorithm in which movement of brighter fireflies is towards the direction of brightness instead of random move. If this direction of brightness is not in the process then firefly is in same position. We call this novel algorithm as MFA-LBG. Experimental results shows that modified firefly algorithm reconstructed image quality and fitness function value is better than the standard firefly algorithm (FA-LBG) and LBG algorithms. It is observed that that modified firefly algorithm convergence time is less than the standard firefly algorithm.

DOI : 10.1007/978-81-322-2731-1_35

Anahtar Kelimeler : Vector quantization, LBG, Firefly algorithm, Modified firefly algorithm, Attractiveness, Fitness function

ISBN: 978-81-322-2731-1
Sayfa: 373-382

Sınıflandırma :

Bilgisayar Bilimi %81

Simülasyon ve Modelleme %90

Comparative Performance Analysis of Optimization Techniques on Vector Quantization for Image Compression

Dergi Makalesi

Kaynak :

IGI Global

Yayıncı :

International Journal of Computer Vision and Image Processing

Yazar(lar) :

Karri Chiranjeevi , Umaranjan Jena , Sonali Dash

Yıl :

2017

Karri Chiranjeevi

Özet Ek Bilgiler