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Abstract

In this study, using cluster centers of the popular clustering algorithms such as K-Means, LBG and Fuzzy C-Means a lossy compression is performed. The performances of these algorithms are improved by the proposed Genetic LBG algorithm. The new algorithm is applied on the standard images and seen that it is better than the classical methods according to both MSE values and visual assessments

Keywords

• K-Means, LBG, Fuzzy C-Means, Genetic, Clustering

GENETiK - LBG ALGORİTMASI İLE SAYISAL GÖRÜNTÜLERİN SIKIŞTIRILMASI - DIGITAL IMAGE COMPRESSION BY GENETIC – LBG ALGORITHM

Abstract

GENETiK - LBG ALGORİTMASI İLE SAYISAL GÖRÜNTÜLERİN SIKIŞTIRILMASI

Bu çalışmada K-Ortalamalar(KO), LBG ve Bulanık C Ortalamalar(BCO) güncel kümeleme algoritmaları yardımı ile bulunan merkezler üzerinden gerçekleştirilen kayıplı görüntü sıkıştırma algoritmalarının performansları, önerilen Genetik LBG Algoritması (GA-LBG) ile iyileştirilmiştir. Önerilen yeni algoritma standart görüntüler üzerinde denenmiş, klasik yöntemlerden hem OKH(Ortalama karesel hata) değerleri, hem de sıkıştırılıp açılan görüntü kalitesi açısından üstün olduğu gözlenmiştir.

DIGITAL IMAGE COMPRESSION BY GENETIC – LBG ALGORITHM

In this study, using cluster centers of the popular clustering algorithms such as K-Means, LBG and Fuzzy C-Means a lossy compression is performed. The performances of these algorithms are improved by the proposed Genetic LBG algorithm. The new algorithm is applied on the standard images and seen that it is better than the classical methods according to both MSE values and visual assessments.

Keywords

• K-Ortalamalar, LBG, Bulanık K-Ortalamalar, Genetik, Kümeleme

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