An Improved Image Re-indexing Technique by Self Organizing Motor Maps

  • Sebastiano Battiato
  • Francesco Rundo
  • Filippo Stanco
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5646)

Abstract

The paper presents a novel Motor Map neural network for re-indexing color mapped images. The overall learning process is able to smooth the local spatial redundancy of the indexes of the input image. Differently than before, the proposed optimization process is specifically devoted to re-organize the matrix of differences of the indexes computed according to some predefined patterns. Experimental results show that the proposed approach achieves good performances both in terms of compression ratio and zero order entropy of local differences. Also its computational complexity is competitive with previous works in the field.

Keywords

Input Image Reward Function Indexing Scheme Lossless Compression Current Entropy 
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.

References

  1. 1.
    Battiato, S., Lukac, R.: Color-Mapped Imaging. In: Furth, B. (ed.) Encyclopedia of Multimedia, pp. 83–88. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  2. 2.
    Pinho, A.J., Neves, A.J.R.: A survey on palette reordering methods for improving the compression of color-indexed images. IEEE Transactions on Image Processing 13(11) (November 2004)Google Scholar
  3. 3.
    Zaccarin, A., Liu, B.: A novel approach for coding color quantized images. IEEE Transactions on Image Processing 2, 442–453 (1993)CrossRefGoogle Scholar
  4. 4.
    Spira, A., Malah, D.: Improved lossless compression of color-mapped images by an approximate solution of the traveling salesman problem. In: IEEE Int. Conf. Acoustics, Speech, Signal Processing, May 2001, vol. III, pp. 1797–1800 (2001)Google Scholar
  5. 5.
    Po, L.M., Tan, W.T.: Block address predictive color quantization image compression. Electron. Lett. 30(2), 120–121 (1994)CrossRefGoogle Scholar
  6. 6.
    Hadenfeldt, A.C., Sayood, K.: Compression of color-mapped images. IEEE Trans. Geosci. Remote Sens. 32, 534–541 (1994)CrossRefGoogle Scholar
  7. 7.
    Lai, J.Z.C., Liaw, Y.-C.: A novel approach of reordering color palette for indexed image compression. IEEE Signal Processing Letters 14(2), 117–120 (2007)CrossRefGoogle Scholar
  8. 8.
    Chuang, W.-H., Pei, S.-C.: A low-complexity palette re-indexing technique based on sampling-swapping. In: Proc. of IEEE International Conference on Image Processing, pp. 1029–1032 (2008)Google Scholar
  9. 9.
    Fojtik, J., Vaclav, H.: Invisible modification of the palette colour image enhancing lossless compression. In: Amin, A., Pudil, P., Dori, D. (eds.) SPR 1998 and SSPR 1998. LNCS, vol. 1451, pp. 1029–1036. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  10. 10.
    Waldemar, P., Ramstad, T.A.: Subband coding of color images with limited palette size. In: IEEE Int. Conf. on Acoustics, Speech, and Signal Processing, ICASSP 1994, April 1994, No. V, pp. 353–356 (1994)Google Scholar
  11. 11.
    Gormish, M.J.: Compression of palletized images by colour. In: Proc. of IEEE International Conference on Image Processing (1995)Google Scholar
  12. 12.
    Memon, N., Venkateswaran, A.: On ordering colour maps for lossless predictive coding. IEEE Trans. Image Proc. 5(11), 1522–1527 (1996)CrossRefGoogle Scholar
  13. 13.
    Zeng, W., Li, J., Lei, S.: An efficient colour re-indexing scheme for palette-based compression. In: Proc. of 7th IEEE International Conference on Image Processing, pp. 476–479 (2000)Google Scholar
  14. 14.
    Battiato, S., Gallo, G., Impoco, G., Stanco, F.: An efficient re-indexing algorithm for color-mapped images. IEEE Transactions on Image Processing 13(11), 1419–1423 (2004)MathSciNetCrossRefGoogle Scholar
  15. 15.
    You, K.-S., Han, D.-S., Jang, E.S., Jang, S.-Y., Lee, S.-K., Kwak, H.-S.: Ranked image generation for arithmetic coding in indexed color image. In: Proceedings of 7th International Workshop on Enterprise networking and Computing in Healthcare Industry, HEALTHCOM, June 2005, pp. 299–302 (2005)Google Scholar
  16. 16.
    Neves, A.J.R., Pinho, A.J.: A bit-plane approach for lossless compression of color-quantized images. In: Proc. of the IEEE Int. Conf. on Acoustics, Speech, and Signal Processing, ICASSP 2006, May 2006, vol. 13 (2006)Google Scholar
  17. 17.
    Battiato, S., Rundo, F., Stanco, F.: Self organizing motor maps for color-mapped image re-indexing. IEEE Transactions on Image Processing 16(12), 2905–2915 (2007)MathSciNetCrossRefGoogle Scholar
  18. 18.
    Arena, P., Fortuna, L., Frasca, M., Sicurella, G.: An adaptive, self-organizing dynamical system for hierarchical control of bio-inspired locomotion. IEEE Transactions on Systems, Man, and Cybernetics, Part B 34, 1823–1837 (2004)CrossRefGoogle Scholar
  19. 19.
    Pinho, A.J., Neves, A.J.R.: On the relation between memon’s and the modified zeng’s palette reordering methods. Image Vision Comput. 24(5), 534–540 (2006)CrossRefGoogle Scholar
  20. 20.
    Pei, S.-C., Chuang, Y.-T., Chuang, W.-H.: Effective palette indexing for image compression using self-organization of kohonen feature map. IEEE Transaction on Image Processing 15(9), 2493–2498 (2006)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Sebastiano Battiato
    • 1
  • Francesco Rundo
    • 1
  • Filippo Stanco
    • 1
  1. 1.Dipartimento di Matematica e InformaticaUniversity of CataniaCataniaItaly

Personalised recommendations