Table of Contents
ISRN Signal Processing
Volume 2011, Article ID 359592, 9 pages
http://dx.doi.org/10.5402/2011/359592
Research Article

Combining Fractal Coding and Orthogonal Linear Transforms

Biometric and Image Processing Laboratory (BIPLab), University of Salerno, via Ponte don Melillo, 84084 Fisciano (SA), Italy

Received 2 February 2011; Accepted 8 March 2011

Academic Editors: M. Barkat and W. Liu

Copyright © 2011 Michele Nappi and Daniel Riccio. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. C. He, S. X. Yang, and X. Huang, “Variance-based accelerating scheme for fractal image encoding,” Electronics Letters, vol. 40, no. 2, pp. 115–116, 2004. View at Publisher · View at Google Scholar
  2. C. M. Lai, K. M. Lam, and W. C. Siu, “Improved searching scheme for fractal image coding,” Electronics Letters, vol. 38, no. 25, pp. 1653–1654, 2002. View at Publisher · View at Google Scholar
  3. M. Polvere and M. Nappi, “Speed-up in fractal image coding: comparison of methods,” IEEE Transactions on Image Processing, vol. 9, no. 6, pp. 1002–1009, 2000. View at Publisher · View at Google Scholar · View at PubMed
  4. C. Aggarwal, “On the effects of dimensionality reduction on high dimensional search,” in Proceedings of the ACMPODS Conference, pp. 1–11, IBM Thomas J. Watson Research Center, Yorktown, NY, USA, 2001.
  5. B. E. Wohlberg and G. de Jager, “Fast image domain fractal compression by DCT domain block matching,” Electronics Letters, vol. 31, no. 11, pp. 869–870, 1995. View at Publisher · View at Google Scholar
  6. J. L. Wu and W. J. Duh, “Feature extraction capability of some discrete transforms,” in Proceedings of the IEEE International Symposium on Circuits and Systems, vol. 5, pp. 2649–2652, June 1991.
  7. D. J. Duh, J. H. Jeng, and S. Y. Chen, “DCT based simple classification scheme for fractal image compression,” Image and Vision Computing, vol. 23, no. 13, pp. 1115–1121, 2005. View at Publisher · View at Google Scholar
  8. J. M. Mas Ribés, B. Simon, and B. Macq, “Combined Kohonen neural networks and discrete cosine transform method for iterated transformation theory,” Signal Processing: Image Communication, vol. 16, no. 7, pp. 643–656, 2001. View at Publisher · View at Google Scholar
  9. Y. Zhao and B. Yuan, “A hybrid image compression scheme combining block-based fractal coding and DCT,” Signal Processing: Image Communication, vol. 8, no. 2, pp. 73–78, 1996. View at Publisher · View at Google Scholar
  10. T. Kim, R. E. Van Dyck, and D. J. Miller, “Hybrid fractal zerotree wavelet image coding,” Signal Processing: Image Communication, vol. 17, no. 4, pp. 347–360, 2002. View at Publisher · View at Google Scholar
  11. Y. Zhang and L. M. Po, “Speeding up fractal image encoding by wavelet-based block classification,” Electronics Letters, vol. 32, no. 23, pp. 2140–2141, 1996. View at Google Scholar
  12. R. Distasi, M. Nappi, and D. Riccio, “A range/domain approximation error-based approach for fractal image compression,” IEEE Transactions on Image Processing, vol. 15, no. 1, pp. 89–97, 2006. View at Publisher · View at Google Scholar
  13. J. L. Bentley, “Multidimensional binary search trees used for associative searching,” Communications of the ACM, vol. 18, no. 9, pp. 509–517, 1975. View at Publisher · View at Google Scholar
  14. J. Kominek, “Waterloo BragZone and Fractals Repository,” January 2007, http://links.uwaterloo.ca/Repository.html.
  15. Y. Iano, F. S. da Silva, and A. L. M. Cruz, “A fast and efficient hybrid fractal-wavelet image coder,” IEEE Transactions on Image Processing, vol. 15, no. 1, pp. 98–105, 2006. View at Publisher · View at Google Scholar