Skip to main content
SpringerLink
Log in

Low bit-rate multi stage vector quantization based on energy clustered training set

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

In this paper, a new multi stage vector quantization with energy clustered training set is proposed for color image coding. The input image is applied with orthogonal polynomials based transformation and the energy clustered transformed training vectors are obtained with reduced dimension. The stage-by-stage codebook for vector quantization is constructed from the proposed transformed training vectors so as to reduce computational complexity. This method also generates a single codebook for all the three color components, utilizing the inter-correlation property of individual color planes and interactions among the color planes due to the proposed transformation. As a result, the color image encoding time is only slightly higher than that of gray scale image coding time and in contrast to the existing color image coding techniques, whose time is thrice greater than that of gray scale image coding. The experimental results reveal that only 35 % and 10 % of transform coefficients are sufficient for smaller and larger blocks respectively, for the reconstruction of images with good quality. The proposed multi stage vector quantization technique is faster when compared to existing techniques and yields better trade-off between image quality and block size for encoding.

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

Similar content being viewed by others

References

  1. Adams MD, Kossentini F (2000) Reversible integer-to-integer wavelet transforms for image compression: performance evaluation and analysis. IEEE Trans Image Process 9:1010–1024

    Article  MATH  MathSciNet  Google Scholar 

  2. Annadurai S, Sundaresan M (2009) Wavelet based enhanced color image compression relying on sub-band vector quantization. ICGST-GVIP J 9:9–16

    Google Scholar 

  3. Barlaud M, Sole P, Gaidon T, Antoni M, Mathieu P (1994) Pyramidal lattice vector quantization for multiscale image coding. IEEE Trans Image Process 3:367–381

    Article  Google Scholar 

  4. Canta GR, Poggi G (1998) Kronecker-product gain-shape vector quantization for multispectral and hyperspectral image coding. IEEE Trans Image Process 7:668–678

    Article  MATH  MathSciNet  Google Scholar 

  5. Chang C-C, Li Y-C, Yeh J-B (2006) Fast codebook search algorithms based on tree-structured vector quantization. Pattern Recognit Lett 27:1077–1086

    Article  Google Scholar 

  6. Courant R, Hilbert D (1975) Methods of mathematical physics, 1st edn. Wiley Eastern, New Delhi

  7. Equitz WH (1989) A new vector quantization clustering algorithm. IEEE Trans Accoust Speech Signal Process 37:1568–1575

    Article  Google Scholar 

  8. Esakkirajan S, Veerakumar T, Senthil Murugan V, Sudhakar R (2006) Image compression using contourlet transform and multi stage vector quantization. GVIP J 6:19–28

    Google Scholar 

  9. Fischer TR (1986) A pyramid vector quantizer. IEEE Trans Inf Theory 32:568–583

    Article  MATH  Google Scholar 

  10. Flanagant JK, Morrell DR (1989) Vector quantization codebook generation using simulated annealing. IEEE Int Conf on Acoust Speech and Signal Process 3:1759–1762

    Article  Google Scholar 

  11. Gersho A, Gray RM (1992) Vector quantization and signal compression. Kluwer Academic Press/Springer

  12. Goldberg M (1986) Image compression using adaptive vector quantization. IEEE Trans Commun 34:180–187

    Article  Google Scholar 

  13. Hsieh C-H (1992) DCT-based codebook design for vector quantization of images. IEEE Trans Circuits Syst Video Technol 2:401–409

    Article  Google Scholar 

  14. Hsieh C-H, Shao W-Y, Jing M-H (2000) Image compression based on multistage vector quantization. J Visual Comm and Image Represent 11:374–384

    Article  Google Scholar 

  15. Huang H-C, Pan J-S, Zhe-Ming L, Sun S-H, Hang H-M (2001) Vector quantization based on genetic simulated annealing. Signal Process 81:1513–1527

    Article  MATH  Google Scholar 

  16. Kim JW, Lee SU (1992) A transform domain classified vector quantizer for image coding. IEEE Trans Circuits Syst Video Technol 2:3–14

    Article  Google Scholar 

  17. Krishnamoorthy R, Kannan N (2009) Codebook generation for vector quantization on orthogonal polynomials based transform coding. Int J Signal Process 5:67–73

    Google Scholar 

  18. Krishnamoorthy R, Punidha R. FITVQSPC: Fast and improved transformed vector quantization using static pattern clustering. Int Conf on Signal Process Image Process and Pattern Recog (2010) 146–155

  19. Krishnamoorthy R, Punidha R. FVQEOPT: Fast vector quantization encoding with orthogonal polynomials transform. Int Conf on Machine Vis. (2010) 92–96

  20. Lai JZC, Liaw Y-C (2009) A novel encoding algorithm for vector quantization using transformed codebook. Pattern Recognit 42:3065–3070

    Article  MATH  Google Scholar 

  21. Lai JZC, Liaw Y-C, Liu J (2008) A fast VQ codebook generation algorithm using codeword displacement. Pattern Recognit 41:315–319

    Article  MATH  Google Scholar 

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

    Article  Google Scholar 

  23. Linde Y, Buzo A, Gray RM (1980) An algorithm for vector quantizer design. IEEE Trans Commun 28:84–94

    Article  Google Scholar 

  24. Liou R-J, Wu J (2008) Quadtree image compression using sub-band DCT features and kohonen neural networks. IEEE Int Conf on Audio Lang and Image Process. 252–256

  25. Wang M, Ma H-P, Zhou C-Q, Yang B (2007) An improved multi-stage vector quantization for image coding. IEEE 3rd Int. Conf. on Intel. Inf. Hiding and Multimed Signal Process. 415–420

  26. Nasrabadi NM, Feng Y (1990) Image compression using address vector quantization. IEEE Trans Comm 38:2166–2173

    Article  Google Scholar 

  27. Pan JS, McInnes FR, Jack MA (1995) VQ codebook design using genetic algorithms. Electronics Lett 31:1418–1419

    Article  Google Scholar 

  28. Salleh MFM, Soraghan J (2007) A new multistage lattice vector quantization with adaptive subband thresholding for image compression. EURASIP J Appl Signal Process 1:1–11

    Google Scholar 

  29. Shen E, Hasegawa O (2006) An adaptive incremental LBG for vector quantization. Neural Netw 19:694–704

    Article  MATH  Google Scholar 

  30. Shen G, Zeng B, Liou M-L (2003) Adaptive vector quantization with codebook updating based on locality and history. IEEE Trans Image Process 12:283–295

    Article  Google Scholar 

  31. Sun H, Lam K-Y, Chung S-L, Dong W, Ming G, Sun J (2005) Efficient vector quantization using genetic algorithm. Neural Comput Appl 14:203–211

    Article  Google Scholar 

  32. Swilem A (2010) A Fast vector quantization encoding algorithm based on projection pyramid with hadamard transformation. Image Vision Comput 28:1637–1644

    Article  Google Scholar 

  33. Tsai W, Lee C-Y (2009) A fast VQ codebook generation algorithm via pattern reduction. Pattern Recognit Lett 30:653–660

    Article  Google Scholar 

  34. Yang S-B (2008) Constrained-storage multistage vector quantization based on genetic algorithms. Pattern Recognit 41:689–700

    Article  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Computer Vision Lab, Department of CSE, Anna University of Technology, Trichy, 620 024, Tamilnadu, India

    R. Krishnamoorthy & R. Punidha

Authors
  1. R. Krishnamoorthy
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. R. Punidha
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to R. Krishnamoorthy.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Krishnamoorthy, R., Punidha, R. Low bit-rate multi stage vector quantization based on energy clustered training set. Multimed Tools Appl 70, 2293–2308 (2014). https://doi.org/10.1007/s11042-012-1244-4

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-012-1244-4

Keywords

Search

Navigation