Advertisement

Color VQ-Based Image Compression by Manifold Learning

  • Christophe Charrier
  • Olivier Lézoray
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6134)

Abstract

When the amount of color data is reduced in a lossy compression scheme, the question of the use of a color distance is crucial, since no total order exists in \(\textrm{I\kern-0.14emR}^n, n>1\). Yet, all existing color distance formulae have severe application limitation, even if they are widely used, and not necesseraly within the initial context they have been developed for. In this paper, a manifold learning approach is applied to reduce the dimension of data in a Vector Quantization approach to obtain data expressed in \(\textrm{I\kern-0.14emR}\). Three different techniques are applied before construct the codebook. Comparaisons with the standard LBG-based VQ method are performed to judge the performance of the proposed approach using PSNR, MS-SSIM and VSNR measures.

Keywords

Dimensionality Reduction Image Quality Assessment Dimensionality Reduction Method Manifold Learn Codebook Size 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Dordrecht (1991)Google Scholar
  2. 2.
    Saul, L.K., Weinberger, K.O., Ham, J., Sha, F., Lee, D.D.: Spectral methods for dimensionnality reduction. In: Semi-supervised Learning, pp. 279–294. MIT Press, Cambridge (2006)Google Scholar
  3. 3.
    Belkin, M., Niyogi, P.: Laplacien eigenmaps for dimensionality reduction and data representation. Neural Computing 15(6), 1373–1396 (2003)zbMATHCrossRefGoogle Scholar
  4. 4.
    Bengio, Y., Vincent, P.: Manifold parzen windows. Tech. Rep., CIRANO (2004)Google Scholar
  5. 5.
    Linde, Y., Buzo, A., Gray, R.R.: An algorithm for vector quantizer design. IEEE Transactions on Communications 28, 84–94 (1980)CrossRefGoogle Scholar
  6. 6.
    Chandler, D.M., Hemami, S.S.: VSNR: A wavelet-based visual signal-to-noise ratio for natural images. IEEE Transactions on Image Processing 16(9), 2284–2298 (2007)CrossRefMathSciNetGoogle Scholar
  7. 7.
    Sheik, H.R., Sabir, M.F., Bovik, A.C.: A statistical evaluation of recent full reference image quality assessment algorithms. IEEE Transactions on Image Processing 5(11), 3441–3452 (2006)Google Scholar
  8. 8.
    Laboratory for Image & Video Engineering, University of Texas (Austin) LIVE Image Quality Assessment Database (2002), http://live.ece.utexas.edu/research/Quality

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Christophe Charrier
    • 1
    • 2
  • Olivier Lézoray
    • 1
  1. 1.Laboratoire GREYC, Unité Mixte de Recherche CNRS 6072Université de Caen Basse-NormandieCaenFrance
  2. 2.Dept. d’informatique, laboratoire MOIVREUniversité de SherbrookeSherbrookeCanada

Personalised recommendations