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

GENETiK - LBG ALGORİTMASI İLE SAYISAL GÖRÜNTÜLERİN SIKIŞTIRILMASIBu 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 ALGORITHMIn 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.

-

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

___

  • 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
Celal Bayar Üniversitesi Fen Bilimleri Dergisi-Cover
  • ISSN: 1305-130X
  • Başlangıç: 2005
  • Yayıncı: Manisa Celal Bayar Üniversitesi Fen Bilimleri Enstitüsü
Sayıdaki Diğer Makaleler

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

İlker KILIÇ

FOURIER SERİLERİNİN EŞLENİK SERİLERİNİN MUTLAK GENELLEŞTİRİLMİŞ NÖRLUND TOPLANABİLMESİ ÜZERİNE - ON ABSOLUTE GENERALIZED NORLUND SUMMABILITY OF THE CONJUGATE SERIES OF FOURIER SERIES

Abdullah SÖNMEZOĞLU

KURŞUN(II) ASETAT KOORDİNASYON POLİMERLERİ - LEAD(II) ACETATE COORDINATION POLYMERS

Şebnem ESEN SÖZERLİ

CuIn0.7Ga0.3(Se0.6Te0.4)2 İNCE FİLMLERİN FARKLI TAVLAMA SICAKLILARI ALTINDA OPTİK, YAPISAL VE MORFOLOJİK KARAKTERİZASYONU

Songül FİAT VAROL, Emin BACAKSIZ, Güven ÇANKAYA, Michael KOMPİTSAS

AN INTEGER PROGRAMMING MODEL FOR THE CONFERENCE TIMETABLING PROBLEM - KONFERANS ÇİZELGELEME PROBLEMİ İÇİN BİR TAMSAYILI PROGRAMLAMA MODELİ

Emrah EDİS, Rahime SANCAR EDİS

ETIAL 180 ALÜMİNYUM ALAŞIMINA İLAVE EDİLEN Mg ve Sn ELEMENTLERİNİN İNTERMETALİK FAZLARA ETKİSİ - THE INFLUENCE OF Sn AND Mg CONTENTS ON THE INTERMETALLIC PHASES OF ETIAL 180 ALLOY

Mustafa BAŞARANEL, Nurşen SAKLAKOĞLU, Simge GENÇALP İRİZALP

OPTICAL, STRUCTURAL AND MORPHOLOGICAL CHARACTERIZATION OF CuIn0.7Ga0.3(Se0.6Te0.4)2 THİN FILMS UNDER DIFFERENT ANNEALING TEMPERATURES - CuIn0.7Ga0.3(Se0.6Te0.4)2 İNCE FİLMLERİN FARKLI TAVLAMA SICAKLILARI ALTINDA OPTİK, YAPISAL VE MORFOLOJİK KARAKTERİZASYO

Songül FİAT VAROL, Emin BACAKSIZ, Güven ÇANKAYA, Michael KOMPİTSAS

RADYO FREKANS TANIMLAMA SİSTEMİNE DAYALI HAMMADDE DEPO YÖNETİMİ - RFID BASED RAW MATERIAL WAREHOUSE MANAGEMENT

Özgür ESKİ, Ceyhun ARAZ, Tayfun DELAN, Levent BAYOĞLU