An Adaptive Multiresolution-Based Multispectral Image Compression Method

  • Jonathan Delcourt
  • Alamin Mansouri
  • Tadeusz Sliwa
  • Yvon Voisin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6134)


This paper deals with the problem of multispectral image compression. In particular, we propose to substitute the built-in JPEG 2000 wavelet transform by an adequate multiresolution analysis that we devise within the Lifting-Scheme framework. We compare the proposed method to the classical wavelet transform within both multi-2D and full-3D compression strategies. The two strategies are combined with a PCA decorrelation stage to optimize their performance. For a consistent evaluation, we use a framework gathering four families of metrics including the largely used PSNR. Good results have been obtained showing the appropriateness of the proposed approach especially for images with large dimensions.


Image Compression Multispectral Image Compression Method Multiresolution Analysis Compression Strategy 
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  1. 1.
    Boliek, M., Christopoulos, C., Majani, E.: JPEG 2000 Part I Final Committee Draft Version 1.0. ISO/IEC JTC, 1 (2000)Google Scholar
  2. 2.
    Boliek, M., Majani, E., Houchin, J.S., Kasner, J., Carlander, M.L.: JPEG 2000 part II final committee draft. ISO/IEC JTC1/SC29/WG1, FCD 15444, 2 (2000)Google Scholar
  3. 3.
    Christopoulos, C., Skodras, A., Ebrahimi, T.: The JPEG 2000 still image coding system: An overview. IEEE Transactions on Consumer Electronics 46(4), 1103–1127 (2000)CrossRefGoogle Scholar
  4. 4.
    Taubman, D.: High performance scalable image compression with EBCOT. IEEE Transactions on Image Processing 9(7), 1158–1170 (2000)CrossRefGoogle Scholar
  5. 5.
    Taubman, D.S., Marcellin, M.W., Rabbani, M.: JPEG2000: Image compression fundamentals, standards and practice. Journal of Electronic Imaging 11, 286 (2002)CrossRefGoogle Scholar
  6. 6.
    Sweldens, W.: The lifting scheme: A construction of second generation wavelets. Technical Report, Department of Mathematics, University of South Carolina, 6 (1995)Google Scholar
  7. 7.
    Sweldens, W., Schroder, P.: Building your own wavelets at home. ACM SIGGRAPH course notes, 15–87 (1996)Google Scholar
  8. 8.
    Daubechies, I., Sweldens, W.: Factoring wavelet transforms into lifting steps. Journal of Fourier Analysis and Applications 4(3), 247–269 (1998)zbMATHCrossRefMathSciNetGoogle Scholar
  9. 9.
    Maslen, M., Abbott, P.: Automation of the lifting factorisation of wavelet transforms. Computer Physics Communications 127(2-3), 309–326 (2000)zbMATHCrossRefGoogle Scholar
  10. 10.
    Said, A., Pearlman, W.A.: A new, fast, and efficient image codec based on set partitioning in hierarchical trees. IEEE Transactions on circuits and systems for video technology 6(3), 243–250 (1996)CrossRefGoogle Scholar
  11. 11.
    Dragotti, L., Poggi, G., Ragozini, A.R.P.: Compression of multispectral images by three-dimensional SPIHT algorithm. IEEE Transactions on Geoscience and Remote Sensing 38(1), 416–428 (2000)CrossRefGoogle Scholar
  12. 12.
    Delcourt, J., Mansouri, A., Sliwa, T., Voisin, Y.: A comparative study and an evaluation framework of multi/hyperspectral image compression. In: 5th International Conference on Signal-Image Technology and Internet-Based Systems, SITIS 2009 (2009)Google Scholar
  13. 13.
    Christophe, E., Léger, D., Mailhes, C.: Quality criteria benchmark for hyperspectral imagery. IEEE Transactions on Geoscience and Remote Sensing 43(9), 2103 (2005)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Jonathan Delcourt
    • 1
  • Alamin Mansouri
    • 1
  • Tadeusz Sliwa
    • 1
  • Yvon Voisin
    • 1
  1. 1.Laboratoire Le2iAuxerre CedexFrance

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