Lifting Filters Adjustment for Lossless Image Compression Applications

  • Oleksiy Pogrebnyak
  • Ignacio Hernández-Bautista
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8827)

Abstract

A method for adjustment of lifting scheme wavelet filters to achieve a higher image lossless compression is presented. The proposed method analyzes the image spectral characteristics and output the suboptimal coefficients to obtain a higher compression ratio in comparison to the standard lifting filters. The analysis follows by spectral pattern recognition with 1-NN classifier. Spectral patterns are of a small fixed length for the entire image permitting thus the optimization of the filter coefficients for different imager sizes. The proposed method was applied to a set of test images obtaining better image compression results in comparison to the standard wavelet lifting filters.

Keywords

lossless image compression lifting scheme pattern recognition 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Sweldens, W.: The lifting scheme: A new philosophy in biorthogonal wavelet constructions. In: Laine, A.F., Unser, M. (eds.) Wavelet Applications in Signal and Image Processing III. Proc. SPIE, vol. 2569, pp. 68–79 (1995)Google Scholar
  2. 2.
    Calderbank, A.R., Daubechies, I., Sweldens, W., Yeo, B.-L: Lossless image compression using integer to integer wavelet transforms. In: Proceedings of International Conference on Image Processing, ICIP1997, October 26-29, pp. 596–599 (1997)Google Scholar
  3. 3.
    Daubechies, I., Sweldens, W.: Factoring Wavelet and Subband Transforms into Lifting Steps. Technical report, Bell Laboratories, Lucent Technologies (1996)Google Scholar
  4. 4.
    Boulgouris, N.V., Tzovaras, D., Strintzis, M.G.: Lossless image compression based on optimal prediction, adaptive lifting, and conditional arithmetic coding. IEEE Trans Image Processing 10(1), 1–14 (2001)CrossRefMATHGoogle Scholar
  5. 5.
    Thielemann, H.: Adaptive construction of wavelets for image compression. Master’s thesis, Martin-Luther-University Halle-Wittenberg, Institute of Computer Science, Germany (2001)Google Scholar
  6. 6.
    Thielemann, H.: Optimally matched wavelets. Ph.D thesis, Universität Bremen, Vorgelegt im Fachbereich 3 (Mathematik und Informatik), Germany (2005)Google Scholar
  7. 7.
    Li, H., Liu, G., Zhang, Z.: Optimization of Integer Wavelet Transforms Based on Difference Correlation Structures. IEEE Trans. Image Processing 14(11), 1831–1847 (2005)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Kitanovski, V., Kseneman, M., Gleich, D., Taskovski, D.: Adaptive Lifting Integer Wavelet Transform for Lossless Image Compression. In: Proc. of 15th International Conference on Systems, Signals and Image Processing IWSSIP 2008, pp. 105–108 (August 2008)Google Scholar
  9. 9.
    Kaaniche, M., Pesquet-Popesku, B., Benazza-Benyhahia, A.: Adaptive lifting scheme with sparse criteria for image coding. EURASIP Journal on Advances in Signal Processing 2012(1), 1–12 (2012)CrossRefGoogle Scholar
  10. 10.
    Calderbank, A.R., Daubechies, I., Sweldens, W., Yeo, B.-L.: Wavelet Transforms That Map Integers to Integers. Applied and Computational Harmonic Analysis 5(3), 332–369 (1998)MathSciNetCrossRefMATHGoogle Scholar
  11. 11.
    Yoo, H., Jeong, J.: A Unified Framework for Wavelet Transform Based on The Lifting Scheme. In: Proc. of IEEE International Conference on Image Processing ICIP 2001, Tessaloniki, Greece, October 7-10, pp. 793–795 (2001)Google Scholar
  12. 12.
    Pogrebnyak, O., Ramírez, P.M.: Adaptive wavelet transform for image compression applications. In: Tescher, A.G. (ed.) Applications of Digital Image Processing XXVI. Proc. SPIE, vol. 5203, pp. 623–630 (August 2003)Google Scholar
  13. 13.
    Shapiro, J.M.: Embedded Image Coding Using Zerotrees Of Wavelet Coefficients. IEEE Transactions on Signal Processing 41(12), 3445–3462 (1993)CrossRefMATHGoogle Scholar
  14. 14.
    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, 243–250 (1996)CrossRefGoogle Scholar
  15. 15.
    David, J.C.: MacKay. Information Theory, Inference, and Learning Algorithms. Cambridge University Press (2003)Google Scholar
  16. 16.
    Ahmed, N., Natarajan, T., Rao, K.R.: Discrete cosine transform. IEEE Transactions on Computers 23(1), 90–93 (1974)MathSciNetCrossRefMATHGoogle Scholar
  17. 17.
    Cover, T.M., Hart, P.E.: Nearest neighbor pattern classification. IEEE Transactions on Information Theory 13(1), 21–27 (1967)CrossRefMATHGoogle Scholar
  18. 18.
    Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, 2nd edn. John Wiley & Sons (1997)Google Scholar
  19. 19.
    Servetto, S.D., Ramchandran, K., Orchard, M.T.: Image coding based on a morphological representation of wavelet data. IEEE Transactions Onimage Processing 8(9), 1161–1174 (1999)CrossRefGoogle Scholar
  20. 20.
    Oktem, L., Oktem, R., Astola, J.: Hierarchical enumerative coding of DCT coefficients. In: Proc. of IEEE International Conference on Acoustic, Speech and Signal Processing ICASSP 2000, vol. 4, pp. 2043–2046 (2000)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Oleksiy Pogrebnyak
    • 1
  • Ignacio Hernández-Bautista
    • 1
  1. 1.Instituto Politecnico NacionalCentro de Investigacion en ComputacionMexico, D.F.Mexico

Personalised recommendations