Advertisement

On Hybrid Directional Transform-Based Intra-band Image Coding

  • Alin Alecu
  • Adrian Munteanu
  • Aleksandra Pižurica
  • Jan Cornelis
  • Peter Schelkens
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4678)

Abstract

In this paper, we propose a generic hybrid oriented-transform and wavelet-based image representation for intra-band image coding. We instantiate for three popular directional transforms having similar powers of approximation but different redundancy factors. For each transform type, we design a compression scheme wherein we exploit intra-band coefficient dependencies. We show that our schemes outperform alternative approaches reported in literature. Moreover, on some images, we report that two of the proposed codec schemes outperform JPEG2000 by over 1dB. Finally, we investigate the trade-off between oversampling and sparsity and show that, at low rates, hybrid coding schemes with transform redundancy factors as high as 1.25 to 5.8 are capable in fact of outperforming JPEG2000 and its critically-sampled wavelets.

Keywords

Discrete Wavelet Transform Compression Scheme Frequency Plane Detail Signal Redundancy Factor 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Mallat, S.: A Theory for Multiresolution Signal Decomposition: The Wavelet Representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 11, 674–693 (1989)zbMATHCrossRefGoogle Scholar
  2. 2.
    Vetterli, M.: Wavelets, approximation and compression. IEEE Signal Processing Magazine 18, 59–73 (2001)CrossRefGoogle Scholar
  3. 3.
    Candès, E.J., Donoho, D.: Ridgelets: a key to higher-dimensional intermittency. Phil. Trans. R. Soc. Lond. A. 357, 2495–2509 (1999)zbMATHCrossRefGoogle Scholar
  4. 4.
    Candès, E.J., Donoho, D.: New Tight Frames of Curvelets and Optimal Representations of Objects with Piecewise C2 Singularities. Comm. Pure Appl. Math. 57, 219–266 (2004)zbMATHCrossRefGoogle Scholar
  5. 5.
    Do, M.N., Vetterli, M.: Contourlets. In: Welland, G.V. (ed.) Beyond Wavelets, Academic Press, London (2003)Google Scholar
  6. 6.
    Le Pennec, E., Mallat, S.: Sparse Geometric Image Representations with Bandelets. IEEE Transactions on Image Processing 14, 423–438 (2005)CrossRefGoogle Scholar
  7. 7.
    Shapiro, J.M.: Embedded Image Coding Using Zerotrees of Wavelet Coefficients. IEEE Transactions on Signal Processing 41, 3445–3462 (1993)zbMATHCrossRefGoogle Scholar
  8. 8.
    Said, A., Pearlman, W.: A New Fast and Efficient Image Codec Based on Set Partitioning in Hierarchical Trees. IEEE Trans. on Circuits and Systems for Video Tech. 6, 243–250 (1996)CrossRefGoogle Scholar
  9. 9.
    Munteanu, A., Cornelis, J., Van der Auwera, G., Cristea, P.: Wavelet Image Compression - The Quadtree Coding Approach. IEEE Transactions on Information Technology in Biomedicine 3, 176–185 (1999)CrossRefGoogle Scholar
  10. 10.
    Pearlman, W.A., Islam, A., Nagaraj, N., Said, A.: Efficient, low-complexity image coding with a set-partitioning embedded block coder. IEEE Trans. Circuits and Systems for Video Technology 14, 1219–1235 (2004)CrossRefGoogle Scholar
  11. 11.
    Taubman, D.: High Performance Scalable Image Compression with EBCOT. IEEE Transactions on Image Processing 9, 1158–1170 (2000)CrossRefGoogle Scholar
  12. 12.
    Wu, X.: High-order context modeling and embedded conditional entropy coding of wavelet coefficients for image compression. In: Thirty-First Asilomar Conference on Signals, Systems & Computers, vol. 2, pp. 1378–1382 (1997)Google Scholar
  13. 13.
    Hsiang, S.-T., Woods, J.W.: Embedded image coding using zeroblocks of subband/wavelet coefficients and context modeling. In: ISCAS. IEEE International Symposium on Circuits and Systems, Geneva, Switzerland, vol. 3, pp. 662–665. IEEE, Los Alamitos (2000)Google Scholar
  14. 14.
    Chappelier, V., Guillemot, C., Marinkovic, S.: Image Coding with Iterated Contourlet and Wavelet Transforms. In: Proc. IEEE International Conf. on Image Processing, Singapore, pp. 3157–3160. IEEE Computer Society Press, Los Alamitos (2004)Google Scholar
  15. 15.
    Liu, Y., Nguyen, T.T., Oraintara, S.: Low Bit-Rate Image Coding Based on Pyramidal Directional Filter Banks. In: Proc. IEEE International Conference on Acoustics, Speech, and Signal Processing, Toulouse, France, IEEE, Los Alamitos (2006)Google Scholar
  16. 16.
    Eslami, R., Radha, H.: Wavelet-based Contourlet Coding using an SPIHT-like Algorithm. In: Proc. of Conference on Information Sciences and Systems, NJ, pp. 784–788 (2004)Google Scholar
  17. 17.
    Do, M.N., Vetterli, M.: The Contourlet Transform: an Efficient Directional Multiresolution Image Representation. IEEE Trans. Image Proc. 14, 2091–2106 (2005)CrossRefGoogle Scholar
  18. 18.
    Lu, Y., Do, M.N.: Multidimensional Directional Filter Banks and Surfacelets. IEEE Trans. Image Processing (to appear)Google Scholar
  19. 19.
    Liu, J., Moulin, P.: Information-Theoretic Analysis of Interscale and Intrascale Dependencies between Image Wavelet Coefficients. IEEE Transactions on Image Processing 10, 1647–1658 (2001)zbMATHCrossRefGoogle Scholar
  20. 20.
    Po, D.D.-Y., Do, M.N.: Directional multiscale modeling of images using the contourlet transform. IEEE Transactions on Image Processing 15, 1610–1620 (2006)CrossRefGoogle Scholar
  21. 21.
    Alecu, A., Munteanu, A., Pizurica, A., Philips, W., Cornelis, J., Schelkens, P.: Information-Theoretic Analysis of Dependencies between Curvelet Coefficients. In: IEEE International Conference on Image Processing (ICIP), Atlanta, GA, USA, pp. 1617–1620. IEEE, Los Alamitos (2006)CrossRefGoogle Scholar
  22. 22.
    Taubman, D., Marcelin, M.W.: JPEG2000: Image Compression Fundamentals, Standards, and Practice. Kluwer Academic Publishers, Norwell, Massachusetts (2002)Google Scholar
  23. 23.
    Candès, E.J., Demanet, L., Donoho, D.L., Ying, L.: Fast Discrete Curvelet Transforms. Applied and Computational Mathematics, California Institute of Technology (2005)Google Scholar
  24. 24.
    Lu, Y., Do, M.N.: CRISP-Contourlets: a Critically Sampled Directional Multiresolution Image Representation. In: Proc. SPIE Conf. on Wavelet Applic. in Signal and Image Proc. X, San Diego, USA (2003)Google Scholar
  25. 25.
    Schelkens, P., Munteanu, A., Barbarien, J., Galca, M., Giro-Nieto, X., Cornelis, J.: Wavelet Coding of Volumetric Medical Datasets. IEEE Trans. on Medical Imag. 22, 441–458 (2003)CrossRefGoogle Scholar
  26. 26.
    Munteanu, A.: Wavelet Image Coding and Multiscale Edge Detection: Algorithms and Applications. PhD Thesis. Vrije Universiteit Brussel, Brussels (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Alin Alecu
    • 1
  • Adrian Munteanu
    • 1
  • Aleksandra Pižurica
    • 2
  • Jan Cornelis
    • 1
  • Peter Schelkens
    • 1
  1. 1.Dept. of Electronics and Informatics, Vrije Universiteit Brussel – Interdisciplinary Institute for Broadband Technology (IBBT), Pleinlaan 2, 1050 BrusselsBelgium
  2. 2.Dept. of Telecommunications and Information Processing, Ghent University, Sint-Pietersnieuwstraat 41, 9000 GentBelgium

Personalised recommendations