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Performance Analysis of Generalized Zerotree Coders Varying the Maximum Zerotree Degree

  • Luca Cicala
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5259)

Abstract

Despite the release of the JPEG-2000 standard, wavelet-based zerotree coders keep being object of intense research because of their conceptual simplicity and excellent performance. Recently, it has been shown that all zerotree coders can be described by specifying the involved data structures (typically, degree-k zerotrees) and a very limited set of tree decomposition rules, leading to the class of generalized zerotree coders (GZC). Thanks to this abstraction, defining and implementing new coders of this class becomes straightforward. In this work, we then investigate, by means of numerical experiments on various types of visual sources, the performance achievable by such coders as a function of the degree of the underlying zerotrees.

Keywords

Multispectral Image Scalable Video Arithmetic Code Code Machine Action Table 
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.

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References

  1. 1.
    Said, A., Pearlman, W.A.: New fast and efficient image codec based on set partioning in hierarchical tree. IEEE Trans. on Circuits and Systems on Video Technology 6(3), 243–250 (1996)CrossRefGoogle Scholar
  2. 2.
    Taubman, D.: High performance scalable image compression with ebcot. IEEE Trans. on Image Processing 9(7), 1158–1170 (2000)CrossRefGoogle Scholar
  3. 3.
    Liu, J., Moulin, P.: Information-theoretic analysis of interscale and intrascale dependencies between image wavelet coefficients. IEEE Trans. On Image Processing 10(11), 1647–1658 (2001)MathSciNetCrossRefzbMATHGoogle Scholar
  4. 4.
    Shapiro, J.M.: Embedded image coding using zerotrees of wavelet coefficients. IEEE Trans. on Signal Processing 41(12), 3445–3463 (1993)CrossRefzbMATHGoogle Scholar
  5. 5.
    Kim, B.-J., Xiong, Z., Pearlman, W.A.: Low bit-rate, scalable video coding with 3d set partioning in hierarchical trees (3d spiht). IEEE Trans. On Circuits and Systems on Video Technology 10(8), 1374–1387 (2000)CrossRefGoogle Scholar
  6. 6.
    Dragotti, P.L., Poggi, G., Ragozini, A.R.P.: Compression of multispectral images by three-dimensional spiht algorithm. IEEE Trans. On Geoscience and Remote Sensing 38(1), 416–428 (2000)CrossRefGoogle Scholar
  7. 7.
    He, C., Dong, J., Zheng, Y.F., Gao, Z.: Optimal 3d coefficient tree structure for 3d wavelet video coding. IEEE Trans. On Circuits and Systems on Video Technology 13(10), 961–972 (2003)CrossRefGoogle Scholar
  8. 8.
    Cagnazzo, M., Poggi, G., Verdoliva, L.: Region-based transform coding of multispectral images. IEEE Trans. on Image Processing 16(12), 2916–2926 (2007)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Danyali, H., Mertins, A.: Highly scalable image compression based on spiht for network applications. In: IEEE International Conference on Image Processing, Rochester,NY, USA (September 2002)Google Scholar
  10. 10.
    Danyali, H., Mertins, A.: Flexible, highly scalable, object-based wavelet image compression algorithm for network applications. Vision, Image and Signal Processing, IEE Proceedings 151(6), 498–510 (2004)CrossRefGoogle Scholar
  11. 11.
    Cho, Y., Pearlman, W.A.: Quantifying the performance of zerotrees of wavelet coefficients: degree-k zerotree model. IEEE Trans. on Signal Processing 55(6), 2425–2431 (2007)Google Scholar
  12. 12.
    Cicala, L., Poggi, G.: A generalization of zerotree coding algorithms. In: Picture Coding Symposium, Lisbon, Portugal (November 2007)Google Scholar
  13. 13.
    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(11), 1219–1235 (2004)CrossRefGoogle Scholar
  14. 14.
    Secker, A., Taubman, D.: Lifting-based invertible motion adaptive transform (limat) framework for highly scalable video compression. IEEE Trans. On Image Processing 12(12), 1530–1542 (2003)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Luca Cicala
    • 1
  1. 1.CIRA, the Italian Aerospace Research CenterCapua (CE)Italy

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