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

Yıl 2013, Cilt: 9 Sayı: 2, 45 - 54, 06.01.2015

İlker Kılıç

Öz

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.

Anahtar Kelimeler

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

Kaynakça

• Lloyd, Stuart P., "Least squares quantization in PCM", IEEE Transactions on Information Theory, 28 (2): 129–137 (1982).
• Kanungo, T., Mount, D. M., Netanyahu, N. S., Piatko, C. D., Silverman, R., Wu, A. Y., “An efficient k-means clustering algorithm: Analysis and implementation”, IEEE Trans. Pattern Analysis and Machine Intelligence, 24: 881–892 (2002).
• Likas A., Vlassis N., Verbeek J.J., “The global k-means clustering algorithm”, Pattern Recognition, 36(2): 451-461 (2003).
• Bagirov, A. M., Ugon, J., Webb, D., “Fast modified global k-means algorithm for incremental cluster construction”, Pattern Recognition, 44(4): 866-876 (2011).
• Bezdek, J.C., Ehrlich, R., Full, W., “ FCM: The Fuzzy C-Means clustering algorithm”, Computers & Geosciences, 10(2-3): 191-203 (1984).
• J. C. Dunn, "A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters", Journal of Cybernetics, 3: 32-57 (1973).
• Ya-zhong, L., Gan H., Jin-ku G.U., “Improved FCM algorithm using difference of neighborhood information”, Journal of Computer Applications, 31(2): 375-378 (2011).
• KangJ., MinL., Luan Q., LiX., LiuJ., “Novel modified fuzzy c-means algorithm with applications”, Digital Signal Processing, 19(2): 309-319 (2009).
• Gray, R.M., “Vector Quantization”, IEEE ASSP Magazine, 1(2): 4-29 (1984).
• Linde Y., Buzo A, Gray R. M., “An Algorithm for Vector Quantizer Design”, IEEE Transactions on Communications, 28: 84-95 (1980).
• Lin, Y.C & Tai, S.C.,“A Fast Linde-Buzo- Gray Algorithm in Image Vector Quantization”, IEEE Transactions on Circiuts and Systems-II : Analog and Digital Signal Processing, 45: 432- 435 (1998).
• Patane G., Russo M., “The enhanced LBG algorithm ”, Neural Networks , 14 : 1219 – 1237(2001).
• Tsai C.W., Lee C.Y., Chiang M.C., Yang C.S.,“A fast VQ codebook generation algorithm via pattern reduction”, Pattern Recognition Letters, 30: 653–660 (2009).
• Pan Z.B., Yu G.H., Li Y., “Improved fast LBG training algorithm in Hadamard domain”, Electronics Letters, 47(8): 488-489 (2011).
• Holland J.H., “Adaptation in Natural and Artificial Systems”, 1975.
• Wang F.H., Jain L.C., Pan J. S., “VQ-based watermarking scheme with genetic codebook partition”, Journal of Network and Computer Applications, 30(1): 4-23 (2007).
• Zhang L., Zheng B., Yang Z., “Codebook design using genetic algorithm and its application to speaker identification”, Electronics Letters, 41(10): 619-620 (2005).
• Franti P., “Genetic algorithm with deterministic crossover for vector quantization”, Pattern Recognition Letters, 21: 61-68 (2000). Geliş Tarihi: 26.08.2013 Kabul Tarihi: 09.12.2013