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The Codebook Design of Image Vector Quantization Based on the Firefly Algorithm

  • Ming-Huwi Horng
  • Ting-Wei Jiang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6423)

Abstract

The vector quantization (VQ) was a powerful technique in the applications of digital image compression. The traditionally widely used method such as the Linde-Buzo-Gray (LBG) algorithm always generated local optimal codebook. This paper proposed a new method based on the firefly algorithm to construct the codebook of vector quantization. The proposed method uses LBG method as the initial of firefly algorithm to develop the VQ algorithm. This method is called FF-LBG algorithm. The FF-LBG algorithm is compared with the other three methods that are LBG, PSO-LBG and HBMO-LBG algorithms. Experimental results showed that the computation of this proposed FF-LBG algorithm is faster than the PSO-LBG, and the HBMO-LBG algorithms. Furthermore, the reconstructured images get higher quality than those generated from the LBG and PSO-LBG algorithms, but there are not significantly different to the HBMO-LBG algorithm.

Keywords

Vector Quantization LBG algorithm Firefly algorithm Particle warm optimization Honey bee mating optimization 

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References

  1. 1.
    Linde, Y., Buzo, A., Gray, R.M.: An Algorithm for Vector Quantizer design. IEEE Transaction on Communications 28, 84–95 (1980)CrossRefGoogle Scholar
  2. 2.
    Chen, Q., Yang, J.G., Gou, J.: Image Compression Method using Improved PSO Vector Quantization. In: Wang, L., Chen, K., S. Ong, Y. (eds.) ICNC 2005. LNCS, vol. 3612, pp. 490–495. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  3. 3.
    Feng, H.H., Chen, C.Y., Ye, F.: Evolutionary Fuzzy Particle Swarm Optimization Vector Quantization Learning Scheme in Image Compression. Expert Systems with Applications 32, 213–222 (2007)CrossRefGoogle Scholar
  4. 4.
    Horng, M.H.: Honey Bee Mating Optimization Vector Quantization Scheme in Image Compression. In: Deng, H., Wang, L., Wang, F.L., Lei, J. (eds.) AICI 2009. LNCS, vol. 5855, pp. 185–194. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  5. 5.
    Yang, X.S.: Nature-inspired metaheuristic algorithms. Luniver Press (2008)Google Scholar
  6. 6.
    Lukasik, S., Zak, S.: Firefly algorithm for continuous constrained optimization tasks. In: Nguyen, N.T., Kowalczyk, R., Chen, S.-M. (eds.) ICCCI 2009. LNCS, vol. 5796, pp. 5–7. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  7. 7.
    Yang, X.S.: Firefly algorithms for multimodal optimization. In: Watanabe, O., Zeugmann, T. (eds.) SAGA 2009. LNCS, vol. 5792, pp. 169–178. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  8. 8.
    Lloyd, S.P.: Least Square Quantization in PCM’s. Bell Telephone Laboratories Paper. Murray Hill, NJ (1957)Google Scholar
  9. 9.
    Abbasss, H.B.: Marriage in Honey-bee Optimization (HBO); a Haplo, etrosis Polygynous Swarming Approach. In: CEC 2001, pp. 207–214 (2001)Google Scholar
  10. 10.
    Jiang, T.W.: The application of image thresholding and vector quantization using honey bee mating optimization. Master thesis of National PingTung Institute of Commerce (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Ming-Huwi Horng
    • 1
  • Ting-Wei Jiang
    • 1
  1. 1.Department of Computer Science and Information EngineeringNational Pingtung Institute of CommercePingTungTaiwan

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