Skip to main content

Advertisement

SpringerLink
Log in

Efficient vector quantization using genetic algorithm

  • Original Article
  • Published:
Neural Computing & Applications Aims and scope Submit manuscript

Abstract

This paper proposes a new codebook generation algorithm for image data compression using a combined scheme of principal component analysis (PCA) and genetic algorithm (GA). The combined scheme makes full use of the near global optimal searching ability of GA and the computation complexity reduction of PCA to compute the codebook. The experimental results show that our algorithm outperforms the popular LBG algorithm in terms of computational efficiency and image compression performance.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. Srinivas M, Patnailk LM (1994) Genetic algorithms: a survey. Computer 27(6):17–26

    Article  Google Scholar 

  2. Koza JR (1995) Survey of genetic algorithms and genetic programming. In: WESCON/’95, pp 589–594

  3. Holland JH (1975) Adaptation in natural and artificial systems. University of Michigan press, Ann Arbor

    Google Scholar 

  4. Liu B (2002) Theory and practice of uncertain programming. Phisica-verlag, Heidelberg

    Google Scholar 

  5. Kinsner W (2002) Compression and its metrics for multimedia. In: IEEE proceedings of ICCI’02, pp 107–121

  6. Linde Y, Buzo A, Gray RM (1980) An algorithm for vector quantizer design. IEEE Trans Commun 28(1):84–95

    Article  Google Scholar 

  7. Gray RM (1984) Vector quantization. IEEE ASSP Mag 1(2):4–29

    Google Scholar 

  8. Nasrabadi NM, King RA (1988) Image coding using vector quantization: a review. IEEE Trans Commun 36(8):957–971

    Article  Google Scholar 

  9. Cosman PC, Gray RM, Vetterli M (1996) Vector quantization of image subbands: a survey. IEEE Trans Image Process 5(2):202–225

    Article  Google Scholar 

  10. Li RY, Kim J, Al-Shamakhi N (2002) Image compression using transformed vector quantization. Image Vis Comput 20:37–45

    Article  Google Scholar 

  11. Rose K, Gurewitz E, Fox GC (1992) Vector Quantization by deterministic annealing. IEEE Trans Inform Theory 38(4):1249–1257

    Article  Google Scholar 

  12. Rose K (1998) Deterministic annealing for clustering, compression, classification, regression and related optimization problems. Proc IEEE 86:2210–2239

    Article  Google Scholar 

  13. Zeger K, Vaisey J, Gersho A (1992) Globally optimal vector quantizer design by stochastic relaxation. IEEE Trans Signal Process 40(2):310–322

    Article  Google Scholar 

  14. Zeger K, Gersho A (1989) Stochastic relaxation algorithm for improved vector quantizier designing. Electron Lett 25(14):896–898

    Google Scholar 

  15. Karayiannis NB, Liu Z (2000) Split and merge codebook design algorithms for image compression. J Electron Imaging 9(4):509–520

    Article  Google Scholar 

  16. Vaisey J, Gersho A (1988) Simulated annealing and codebook design. In: Proceedings ICASSP’88, pp 1176–1179

  17. Ma CK, Chan CK (1991) Maximum descent method for image vector quantization. Electron Lett 27(12):1772–1773

    Google Scholar 

  18. Pan JS, Mcinnes FR, Jack MA (1996) Application of parallel genetic algorithm and property of multiple global optimal to VQ codevector index assignment for noisy channels. Electron Lett 32(4):296–297

    Article  Google Scholar 

  19. Delport V, Koschorreck M (1995) Genetic algorithm for codebook design in vector quantization. Electron Lett 31(2):84–85

    Article  Google Scholar 

  20. Chang CC, Lin DC, Chen TS (1997) An improved VQ Codebook search algorithm using principal component analysis. J Vis Commun Image Represent 8(1):27–37

    Article  Google Scholar 

  21. Wu X (1992) Vector quantizer design by constrained global optimization. In: IEEE proceedings of DCC ’92, pp 132–141

  22. Lee RCT, Chin YH, Chang SC (1976) Application of principal component analysis to multikey searching. IEEE Trans Softw Eng 2(3):185–193

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Software, Tsinghua University, Beijing, People’s Republic of China

    Hongwei Sun, Kwok-Yan Lam, Weiming Dong, Ming Gu & Jiaguang Sun

  2. School of Business and Administration, The Open University of Hong Kong, Kowloon, Hong Kong

    Siu-Leung Chung

Authors
  1. Hongwei Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kwok-Yan Lam
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Siu-Leung Chung
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Weiming Dong
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Ming Gu
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Jiaguang Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Siu-Leung Chung.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Sun, H., Lam, KY., Chung, SL. et al. Efficient vector quantization using genetic algorithm. Neural Comput & Applic 14, 203–211 (2005). https://doi.org/10.1007/s00521-004-0455-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-004-0455-7

Keywords

Search

Navigation