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LEVY UÇUŞLU MEYVE SİNEĞİ ALGORİTMASI İLE GÖRÜNTÜ SIKIŞTIRMA

Year 2022, Volume: 9 Issue: 16, 37 - 48, 14.04.2022

İlker Kılıç

https://doi.org/10.54365/adyumbd.980173

Abstract

Sayısal görüntülerin sıkıştırılıp arşivlenmesi günümüz teknolojisinde çok önemli bir ihtiyaç haline gelmiştir. Son yıllarda doğadan esinlenerek geliştirilen PSO(Parçacık sürü optimizasyonu), MSO(Meyve sineği optimizasyonu), ABO(Ateşböceği optimizasyonu), GA(Genetik Algoritma) gibi sezgisel metodlar da vektör tabanlı görüntü sıkıştırma için kullanılmaya başlamıştır. Bu çalışmada MSO, meyve sineklerinin sorunsuz bir şekilde global optimum noktaya ulaşabilmesi için Levy Uçuşu tekniği ile birleştirilmiştir. MSO algoritmasının en büyük sorunlarından biri de lokal minimum noktaya takılıp global minimuma ulaşamamasıdır. Çoğu zaman küçük nadiren de büyük yarıçap değeri veren Levy Fonksiyonu yardımı ile meyve sineği lokal minimum noktaya hiç takılmayıp global minimum noktayı garantilemektedir. Bu yeni geliştirilen LMSO(Levy uçuşlu meyve sineği optimizasyonu) tekniği standart görüntüler üzerinde test edilmiş ve aynı sıkıştırma oranlarında MSE, PSNR ölçütleri kullanıldığında diğer sezgisel algoritmalardan üstün olduğu gösterilmiştir.

Keywords

Levy Uçuşu Görüntü Sıkıştırma Meyve Sineği Optimizasyonu Vektör Nicemleme

References

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  • Referans2 Linde, Y., Buzo, A., & Gray, R. M. An algorithm for vector quantizer design. IEEE Transaction on Communications. 1980; 28(1): 84–95.
  • Referans3 Lin, Y.C & Tai, S.C. A Fast Linde–Buzo–Gray Algorithmin Image Vector Quantization. IEEE Transactions on Circuits and Systems-II: Analog and Digital Signal Processing. 1998; 45(3): 432-435.
  • Referans4 Patane, G. & Russo, M.. The anhanced LBG algorithm. Neural Networks. 2001; 14; 1219-1237.
  • Referans5 Xu, W.,Nandi, A.K., et.al. Novel vector quantiser design using reinforced learning. Signal Processing. 2005; 85; 1315–1333.
  • Referans6 Tsai, C.W., Lee, C.Y., et.al. A fast VQ codebook generation algorithm via pattern reduction. Pattern Recognition Letters. 2009; 30: 653–660.
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  • Referans8 Karayiannis, N. B., & Bezdek, J. C. An integrated approach to fuzzy learning vector quantization and fuzzy c-means clustering. IEEE Transactions on Fuzzy Systems. 1997; 5(4): 622–628.
  • Referans9 Tsekouras, G.E. A fuzzy vector quantization approach to image compression. Applied Mathematics and Computation. 2005;167: 539–560.
  • Referans10 Kuo, R.C., Wang, H. S. et.al. Application of ant K-Means on clustering analysis. Computers and Mathematics with Applications. 2005; 50; 1709-1724.
  • Referans11 Goldberg DE. Genetic algorithms in search, optimization and machine learning. Addison-Wesley; 1989.
  • Referans12 Sun H., Lam, K.Y., et.al. Efficient vector quantization using genetic algorithm. 2005; 14: 203-211.
  • Referans13 L. Zhang, B. Zheng and Z. Yang. Codebook design using genetic algorithm and its application to speaker identification. Electronics Letters. 2005; 41(10): 619-620.
  • Referans14 Yang S. B., Constrained-storage multistage vector quantization based on genetic algorithms. Pattern Recognition. 2008; 41: 689 – 700.
  • Referans15 Huang, H.C., Pan, J.S., et.al. Vector quantization based on genetic simulated annealing. Signal Processing. 2001; 81: 1513-1523.
  • Referans16 Feng H.M., Chen C.Y., Ye, F. Evolutionary fuzzy particle swarm optimization vector quantization learning scheme in image compression. Expert Systems with Applications. 2007; 32: 213–222.
  • Referans17 Horng, M. H., Jiang, T.W. Image vector quantization algorithm via honey bee mating optimization. Expert Systems with Applications. 2011; 38: 1382–1392.
  • Referans18 Rani, M. L. P., Rao, G. S., & Rao, B. P. An efficient codebook generation using firefly Algorithm for optimum medical image compression. Journal of Ambient Intelligence and Humanized Computing, 2020;1-13.
  • Referans19 Tsai, C.W., Tseng, S.P., et.al. PREACO: A fast ant colony optimization for codebook generation. Applied Soft Computing. 2013; 13: 3008–3020.
  • Referans20 Dai, H., Zhao, G., Lu, J., & Dai, S. Comment and improvement on “A new fruit fly optimization algorithm: taking the financial distress model as an example”. Knowledge - Based Systems, 2014; 59: 159-160.
  • Referans21 Li, H., Guo, S., Li, C., Sun, J., A hybrid annual power load forecasting model based on generalized regression neural network with fruit fly optimization algorithm. Knowledge Based Systems. 2013; 37: 378–387.
  • Referans22 S.M. Lin, Analysis of service satisfaction in web auction logistics service using a combination of fruit fly optimization algorithm and general regression neural network, Neural Computational Applications. 2013; 7: 459–465.
  • Referans23 Jiang, W., Wu, X., Gong, Y., Yu, W., & Zhong, X. Holt–Winters smoothing enhanced by fruit fly optimization algorithm to forecast monthly electricity consumption. Energy, 2020; 193: 116779.
  • Referans24 Li, C., Xu, S., Li, W., L. Hu, L. A novel modified fruit fly optimization algorithm for designing the self-tuning proportional integral derivative controller. Journal of Convergence Information Technology. 2012; 7: 69–77.
  • Referans25 Sheng, W., Bao, Y. Fruit fly optimization algorithm based fractional order fuzzy-PID controller for electronic throttle. Nonlinear Dynamics. 2013; 73: 611-619.
  • Referans26 Chen, P.W, Lin, W.Y., Huang, T.H., Pan, W.T. Using fruit fly optimization algorithm optimized grey model neural network to perform satisfaction analysis for e-business service, Applied Mathematics and Information Sciences. 2013; 7(21): 459–465.
  • Referans27 Meng, T ., & Pan, Q. K. An improved fruit fly optimization algorithm for solving the multidimensional knapsack problem. Applied Soft Computing, 2017; 50: 79-93.
  • Referans28 Yuan, X., Dai, X., Zhao, J., He, Q. On a novel multi-swarm fruit fly optimization algorithm and its application. Applied Mathematics and Computation. 2014; 233: 260–271.
  • Referans29 Wang, L., Xiong, Y., Li, S., & Zeng, Y. R. New fruit fly optimization algorithm with joint search strategies for function optimization problems. Knowledge-Based Systems, 2019; 176: 77-96.
  • Referans30 Sheng, W, Bao, Y., Fruit fly optimization algorithm based fractional order fuzzy – pid controller for electronic throttle, Nonlinear Dynam. 2013;73 (1–2) : 611–619.
  • Referans31 Li, J. Q., Pan, Q. K., & Mao, K. A hybrid fruit fly optimization algorithm for the realistic hybrid flowshop rescheduling problem in steelmaking systems. IEEE Transactions on Automation Science and Engineering, 2015; 13(2): 932-949.
  • Referans32 Ingaleshwar, S., Dharwadkar, N. V., & Jayadevappa , D. Water chaotic fruit fly optimization-based deep convolutional neural network for image watermarking using wavelet transform. Multimedia Tools and Applications, 2021; 1-25.
  • Referans33 Kumar, S. N., Fred, A. L., Kumar, H. A., Varghese, P. S., & Daniel, A . V. BAT Optimization-Based Vector Quantization Algorithm for Compression of CT Medical Images. In ICTMI 2017 (pp. 53-64). Springer, Singapore. 2019
  • Referans34 Metzler, R., Aleksei, V. C. et.al. Some fundamental aspects of Lévy Flights. Chaos, Solitons and Fractals. 2007; 34; 129–142.
  • Referans35 Yang, X.-S. Firefly Algorithm, Lévy Flights and Global Optimization. Research and Development in Intelligent Systems XXVI (Eds M. Bramer, R. Ellis, M. Petridis), Springer. 2010; 209-218.
  • Referans36 Chiranjeevi, K., Jena, U. R. Image compression based on vector quantization using cuckoo search optimization technique. Ain Shams Engineering Journal. 2018; 9(4): 1417-1431.
  • Referans37 Fu, Y., Zhou M., Guo, X., Qi, L. Stochastic multi-objective integrated disassembly-reprocessing reassembly scheduling via fruit fly optimization algorithm. Journal of Cleaner Production. 2021; 278: 123364
  • Referans38 Zhang X., Xu, Y., Caiyang Yu, C., et.al. Gaussian mutational chaotic fruit fly - built optimization and feature selection. Expert Systems With Applications. 2020; 141; 112976.
  • Referans39 Wang, L., Xiong Y., Li, S., et. al. New fruit fly optimization algorithm with joint search strategies for function optimization problems. Knowledge-Based Systems, 2019; 176: 77–96.
  • Referans40 Ding, G., Dong, F., Zou, H. Fruit fly optimization algorithm based on a hybrid adaptive cooperative learning and its application in multilevel image thresholding. Applied Soft Computing Journal, 84 (2019) 105704.

Year 2022, Volume: 9 Issue: 16, 37 - 48, 14.04.2022

İlker Kılıç

https://doi.org/10.54365/adyumbd.980173

Abstract

References

  • Referans1 Gray, R. M. Vector quantization. IEEE ASSP Magazine. 1984;1: 4-29.
  • Referans2 Linde, Y., Buzo, A., & Gray, R. M. An algorithm for vector quantizer design. IEEE Transaction on Communications. 1980; 28(1): 84–95.
  • Referans3 Lin, Y.C & Tai, S.C. A Fast Linde–Buzo–Gray Algorithmin Image Vector Quantization. IEEE Transactions on Circuits and Systems-II: Analog and Digital Signal Processing. 1998; 45(3): 432-435.
  • Referans4 Patane, G. & Russo, M.. The anhanced LBG algorithm. Neural Networks. 2001; 14; 1219-1237.
  • Referans5 Xu, W.,Nandi, A.K., et.al. Novel vector quantiser design using reinforced learning. Signal Processing. 2005; 85; 1315–1333.
  • Referans6 Tsai, C.W., Lee, C.Y., et.al. A fast VQ codebook generation algorithm via pattern reduction. Pattern Recognition Letters. 2009; 30: 653–660.
  • Referans7 Karayiannis, N. B., & Pai, P. I. Fuzzy vector quantization algorithms and their application in image compression. IEEE Transactions in Image Processing. 1995;. 4(9): 1193–1201.
  • Referans8 Karayiannis, N. B., & Bezdek, J. C. An integrated approach to fuzzy learning vector quantization and fuzzy c-means clustering. IEEE Transactions on Fuzzy Systems. 1997; 5(4): 622–628.
  • Referans9 Tsekouras, G.E. A fuzzy vector quantization approach to image compression. Applied Mathematics and Computation. 2005;167: 539–560.
  • Referans10 Kuo, R.C., Wang, H. S. et.al. Application of ant K-Means on clustering analysis. Computers and Mathematics with Applications. 2005; 50; 1709-1724.
  • Referans11 Goldberg DE. Genetic algorithms in search, optimization and machine learning. Addison-Wesley; 1989.
  • Referans12 Sun H., Lam, K.Y., et.al. Efficient vector quantization using genetic algorithm. 2005; 14: 203-211.
  • Referans13 L. Zhang, B. Zheng and Z. Yang. Codebook design using genetic algorithm and its application to speaker identification. Electronics Letters. 2005; 41(10): 619-620.
  • Referans14 Yang S. B., Constrained-storage multistage vector quantization based on genetic algorithms. Pattern Recognition. 2008; 41: 689 – 700.
  • Referans15 Huang, H.C., Pan, J.S., et.al. Vector quantization based on genetic simulated annealing. Signal Processing. 2001; 81: 1513-1523.
  • Referans16 Feng H.M., Chen C.Y., Ye, F. Evolutionary fuzzy particle swarm optimization vector quantization learning scheme in image compression. Expert Systems with Applications. 2007; 32: 213–222.
  • Referans17 Horng, M. H., Jiang, T.W. Image vector quantization algorithm via honey bee mating optimization. Expert Systems with Applications. 2011; 38: 1382–1392.
  • Referans18 Rani, M. L. P., Rao, G. S., & Rao, B. P. An efficient codebook generation using firefly Algorithm for optimum medical image compression. Journal of Ambient Intelligence and Humanized Computing, 2020;1-13.
  • Referans19 Tsai, C.W., Tseng, S.P., et.al. PREACO: A fast ant colony optimization for codebook generation. Applied Soft Computing. 2013; 13: 3008–3020.
  • Referans20 Dai, H., Zhao, G., Lu, J., & Dai, S. Comment and improvement on “A new fruit fly optimization algorithm: taking the financial distress model as an example”. Knowledge - Based Systems, 2014; 59: 159-160.
  • Referans21 Li, H., Guo, S., Li, C., Sun, J., A hybrid annual power load forecasting model based on generalized regression neural network with fruit fly optimization algorithm. Knowledge Based Systems. 2013; 37: 378–387.
  • Referans22 S.M. Lin, Analysis of service satisfaction in web auction logistics service using a combination of fruit fly optimization algorithm and general regression neural network, Neural Computational Applications. 2013; 7: 459–465.
  • Referans23 Jiang, W., Wu, X., Gong, Y., Yu, W., & Zhong, X. Holt–Winters smoothing enhanced by fruit fly optimization algorithm to forecast monthly electricity consumption. Energy, 2020; 193: 116779.
  • Referans24 Li, C., Xu, S., Li, W., L. Hu, L. A novel modified fruit fly optimization algorithm for designing the self-tuning proportional integral derivative controller. Journal of Convergence Information Technology. 2012; 7: 69–77.
  • Referans25 Sheng, W., Bao, Y. Fruit fly optimization algorithm based fractional order fuzzy-PID controller for electronic throttle. Nonlinear Dynamics. 2013; 73: 611-619.
  • Referans26 Chen, P.W, Lin, W.Y., Huang, T.H., Pan, W.T. Using fruit fly optimization algorithm optimized grey model neural network to perform satisfaction analysis for e-business service, Applied Mathematics and Information Sciences. 2013; 7(21): 459–465.
  • Referans27 Meng, T ., & Pan, Q. K. An improved fruit fly optimization algorithm for solving the multidimensional knapsack problem. Applied Soft Computing, 2017; 50: 79-93.
  • Referans28 Yuan, X., Dai, X., Zhao, J., He, Q. On a novel multi-swarm fruit fly optimization algorithm and its application. Applied Mathematics and Computation. 2014; 233: 260–271.
  • Referans29 Wang, L., Xiong, Y., Li, S., & Zeng, Y. R. New fruit fly optimization algorithm with joint search strategies for function optimization problems. Knowledge-Based Systems, 2019; 176: 77-96.
  • Referans30 Sheng, W, Bao, Y., Fruit fly optimization algorithm based fractional order fuzzy – pid controller for electronic throttle, Nonlinear Dynam. 2013;73 (1–2) : 611–619.
  • Referans31 Li, J. Q., Pan, Q. K., & Mao, K. A hybrid fruit fly optimization algorithm for the realistic hybrid flowshop rescheduling problem in steelmaking systems. IEEE Transactions on Automation Science and Engineering, 2015; 13(2): 932-949.
  • Referans32 Ingaleshwar, S., Dharwadkar, N. V., & Jayadevappa , D. Water chaotic fruit fly optimization-based deep convolutional neural network for image watermarking using wavelet transform. Multimedia Tools and Applications, 2021; 1-25.
  • Referans33 Kumar, S. N., Fred, A. L., Kumar, H. A., Varghese, P. S., & Daniel, A . V. BAT Optimization-Based Vector Quantization Algorithm for Compression of CT Medical Images. In ICTMI 2017 (pp. 53-64). Springer, Singapore. 2019
  • Referans34 Metzler, R., Aleksei, V. C. et.al. Some fundamental aspects of Lévy Flights. Chaos, Solitons and Fractals. 2007; 34; 129–142.
  • Referans35 Yang, X.-S. Firefly Algorithm, Lévy Flights and Global Optimization. Research and Development in Intelligent Systems XXVI (Eds M. Bramer, R. Ellis, M. Petridis), Springer. 2010; 209-218.
  • Referans36 Chiranjeevi, K., Jena, U. R. Image compression based on vector quantization using cuckoo search optimization technique. Ain Shams Engineering Journal. 2018; 9(4): 1417-1431.
  • Referans37 Fu, Y., Zhou M., Guo, X., Qi, L. Stochastic multi-objective integrated disassembly-reprocessing reassembly scheduling via fruit fly optimization algorithm. Journal of Cleaner Production. 2021; 278: 123364
  • Referans38 Zhang X., Xu, Y., Caiyang Yu, C., et.al. Gaussian mutational chaotic fruit fly - built optimization and feature selection. Expert Systems With Applications. 2020; 141; 112976.
  • Referans39 Wang, L., Xiong Y., Li, S., et. al. New fruit fly optimization algorithm with joint search strategies for function optimization problems. Knowledge-Based Systems, 2019; 176: 77–96.
  • Referans40 Ding, G., Dong, F., Zou, H. Fruit fly optimization algorithm based on a hybrid adaptive cooperative learning and its application in multilevel image thresholding. Applied Soft Computing Journal, 84 (2019) 105704.
There are 40 citations in total.

Details

Primary Language Turkish
Subjects Engineering
Journal Section Makaleler
Authors

İlker Kılıç 0000-0003-3978-4829

Publication Date April 14, 2022
Submission Date August 7, 2021
Published in Issue Year 2022 Volume: 9 Issue: 16

Cite

APA Kılıç, İ. (2022). LEVY UÇUŞLU MEYVE SİNEĞİ ALGORİTMASI İLE GÖRÜNTÜ SIKIŞTIRMA. Adıyaman Üniversitesi Mühendislik Bilimleri Dergisi, 9(16), 37-48. https://doi.org/10.54365/adyumbd.980173
AMA Kılıç İ. LEVY UÇUŞLU MEYVE SİNEĞİ ALGORİTMASI İLE GÖRÜNTÜ SIKIŞTIRMA. Adıyaman Üniversitesi Mühendislik Bilimleri Dergisi. April 2022;9(16):37-48. doi:10.54365/adyumbd.980173
Chicago Kılıç, İlker. “LEVY UÇUŞLU MEYVE SİNEĞİ ALGORİTMASI İLE GÖRÜNTÜ SIKIŞTIRMA”. Adıyaman Üniversitesi Mühendislik Bilimleri Dergisi 9, no. 16 (April 2022): 37-48. https://doi.org/10.54365/adyumbd.980173.
EndNote Kılıç İ (April 1, 2022) LEVY UÇUŞLU MEYVE SİNEĞİ ALGORİTMASI İLE GÖRÜNTÜ SIKIŞTIRMA. Adıyaman Üniversitesi Mühendislik Bilimleri Dergisi 9 16 37–48.
IEEE İ. Kılıç, “LEVY UÇUŞLU MEYVE SİNEĞİ ALGORİTMASI İLE GÖRÜNTÜ SIKIŞTIRMA”, Adıyaman Üniversitesi Mühendislik Bilimleri Dergisi, vol. 9, no. 16, pp. 37–48, 2022, doi: 10.54365/adyumbd.980173.
ISNAD Kılıç, İlker. “LEVY UÇUŞLU MEYVE SİNEĞİ ALGORİTMASI İLE GÖRÜNTÜ SIKIŞTIRMA”. Adıyaman Üniversitesi Mühendislik Bilimleri Dergisi 9/16 (April 2022), 37-48. https://doi.org/10.54365/adyumbd.980173.
JAMA Kılıç İ. LEVY UÇUŞLU MEYVE SİNEĞİ ALGORİTMASI İLE GÖRÜNTÜ SIKIŞTIRMA. Adıyaman Üniversitesi Mühendislik Bilimleri Dergisi. 2022;9:37–48.
MLA Kılıç, İlker. “LEVY UÇUŞLU MEYVE SİNEĞİ ALGORİTMASI İLE GÖRÜNTÜ SIKIŞTIRMA”. Adıyaman Üniversitesi Mühendislik Bilimleri Dergisi, vol. 9, no. 16, 2022, pp. 37-48, doi:10.54365/adyumbd.980173.
Vancouver Kılıç İ. LEVY UÇUŞLU MEYVE SİNEĞİ ALGORİTMASI İLE GÖRÜNTÜ SIKIŞTIRMA. Adıyaman Üniversitesi Mühendislik Bilimleri Dergisi. 2022;9(16):37-48.
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