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Reseach Article

A Multi-agent System for Image Compression

by Redjimi Mohammed
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 53 - Number 9
Year of Publication: 2012
Authors: Redjimi Mohammed
10.5120/8451-2248

Redjimi Mohammed . A Multi-agent System for Image Compression. International Journal of Computer Applications. 53, 9 ( September 2012), 38-45. DOI=10.5120/8451-2248

@article{ 10.5120/8451-2248,
author = { Redjimi Mohammed },
title = { A Multi-agent System for Image Compression },
journal = { International Journal of Computer Applications },
issue_date = { September 2012 },
volume = { 53 },
number = { 9 },
month = { September },
year = { 2012 },
issn = { 0975-8887 },
pages = { 38-45 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume53/number9/8451-2248/ },
doi = { 10.5120/8451-2248 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:53:42.441527+05:30
%A Redjimi Mohammed
%T A Multi-agent System for Image Compression
%J International Journal of Computer Applications
%@ 0975-8887
%V 53
%N 9
%P 38-45
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

We describe, in this paper, a multi-agent system (MAS) dedicated to the compression of digital images. An Image may be considered as a matrix of N lines and of M columns, that is to say a size of NxM pixels. To each line, we associate an agent. Our approach is inspired from the RLE (Run Length Encoding) method but instead of storing the occurrences of the pixels having a same value followed by the value of these pixels, we use a labeling table which contains all the values of the pixels of the image. We obtain a compressed image as a list of color labels followed by the number of successive occurrences of this color. We observe, that, an image is made up of about thirty different colors approximately, this mean that the label of color can be coded on five bits. This method is very effective for images where the color is coded on several bytes. The algorithm is as follow: each agent traverses the image line with which it is associated by seeking for each detected color the number of its successive occurrences. The agent thus draws up a set of lists for each color found including the number of its occurrences. All the other agents carry out these tasks in parallel. It is possible to obtain images compressed without or with loss of information according to the desired quality of the compressed image. This approach allows a very great improvement of the performances in storage capacity of information (the compressed images thus obtained occupy less memory space) and in execution time (the parallel execution allows very high accelerations of the algorithms).

References
  1. Foley, J. D. , Van Dam, A. and al. 1990 'Computer Graphics: Principles and Practice'. Addison-Welsey, Reading, MA, 2ndEdition.
  2. Jain, A. K. 1989. 'Fundemantals of digital image coding'. Prentice Hall.
  3. Mac Kay, D. J. C. January 1995 'Information Theory, Inference and Learning Algorithms' Cavendish Laboratory, Cambridge, Great Britain.
  4. Ferber, J. 1999. 'Multi-Agent Systems. An Introduction to Distributed Artificial Intelligence'. Addison Wesley, London,
  5. Ferber, J. 1995 'Les systèmes multi-agents. Vers une intelligence collective'. InterEditions, Paris. (in french)
  6. Ferber, J. and Gutknecht, O. 1998 'Aalaadin: a meta-model for the analysis and design of organizations in multi-agent systems', ICMAS 98 (International Conference on Multi-Agent Systems), Paris, Y. Demazeau (ed), IEEE Press, pp. 128-135.
  7. Ferber, J. and Gutknecht, O. 2000 'Operational Semantics of a Role-Based Agent Architecture (best paper award)'. Agent Theories, Architectures and Languages (ATAL 99), N. Jennings and Y. Lespérance Eds, Orlando, Springer-Verlag, LNAI, 1757, pp. 205-217.
  8. Gutknecht, O. , Ferber, J. and Michel, F. 2000 'Madkit: une expérience d'architecture de plate-forme multi-agent générique. 8ème Journées Francophones sur l'Intelligence Artificielle Distribuée et les Systèmes Multi-Agents' (JFIADSMA'2000), La Réunion, Hermès, pp. 223-236, (in French)
  9. Shannon, C. E. July and October 1948 'A mathematical theory of communication' Bell System Technical Journal,vol. 27, pp. 379-423 and 623-656.
  10. Salomon, D. 2004 'Data compression, the complete reference', Springer.
  11. Sayood, K. 2006 'Introduction to Data compression' Morgan Kaufmann Series in Multimedia Information and Systems
  12. Golomb, S. 1966. 'Run-length encoding'. IEEE Trans. Inform. Theory IT-12, pp. 399–401
  13. Nourani, N. and Tehranipour, M. H. , January 2005 'RL-Huffman Encoding for Test Compressionand Power Reduction in Scan Applications' ACM Transactions on Design Automation of Electronic Systems, Vol. 10, No. 1, pp. 91–115.
  14. Huffman , D. 1952. 'A method for the construction of minimum redundancy codes'. In Proc. IRE,40, 9, 1098–1101.
  15. Knieser, M. , Wolff, F. , Papachristou, C. ,Weyer, D. , and Mcintyre, D. 2003. 'A technique for high ratio LZW compression'. In Proceedings of the Design, Automation and Test in Europe (DATE'03). Pp. 116–121.
  16. Pennebaker, W. B. and Mitchell J. L. , 1993 ' JPEG: still image compression standard'. New York: Van Nostrand Reinhold.
  17. Wallace, G. K. April 1991 'The JPEG Still Picture Compression Standard', Comm. ACM, vol. 34, no. 4, pp. 30-44.
  18. Buchsbaum, W. H. 1975 'Color TV Servicing', third edition. Englewood Cliffs, NJ: Prentice Hall,. ISBN 0-13-152397-X
  19. Ahmed, N. , Natarajan, T. and Rao, K. R. Jan. 1974 'Discrete Cosine Transform', IEEE Trans. Computers, vol. C-23, pp 90-93
  20. Mandelbrot, B. 1977. 'Fractals: form, chance and dimension'. San Francisco CA and Reading UK: W. H. Freeman & Co.
  21. Mandelbrot, B. 1985. 'Fractals: Basic Concepts, Computation and Rendering' . Notes for a course given in San Francisco CA on July 23, 1985 at SIGGRAPH 85. (Association for Computing Machinery; Special Interest Group on Computer Graphics. )
  22. Mandelbrot, B. 1982 'The fractal geometry of nature'. New York NY and Oxford UK: W. H. Freeman & Co.
  23. Barnsley, M. F. and Sloan, A. D. , 1988 'A better way to compress images', BYTE magazine, pp 215-223.
  24. Barnsley, M. F. 1988 'Fractal every where', New-york: Academic Press, California.
  25. Davoine F. , Antonini M. , Chassery J. M. and Barland, M. 1996 'Fractal Image compression Based on Delaunay Triangulation and Vector Quantization'. IEEE Trans. image Processing, Vol. 5, N° 2, pp 338-346.
  26. Duh, D. J. , Jeng J. H. and Chen S. Y. 2005 'DCT based simple classification scheme for fractal image compression', Image and vision computing 23 , P. 1115-1121.
  27. Fisher, Y. 1995 'Fractal encoding with quadtree' Chapter 3 in Fractal Image Compression: Theory and Applications to Digital Images', Yuval Fisher, Ed, New York: Springer-Verlag, pp. 55-77.
  28. Fisher, Y. and Menlove, S. , 1995 'Fractal encoding with HV partitions', Chapter 6 in Fractal Image Compression: Theory and Applications to Digital Images, Yuval Fisher, Ed, New York: Springer-Verlag, pp. 119-136.
  29. Fisher, Y. 1995 'Fractal Image Compression : Theory and Application', Springer-verlag, New York: Springer-Verlag, 1995, 341 P.
  30. Hassaballah, M. , Makky, M. M. and Mahdy, B. Y. 2005 'A Fast Fractal Image Compression Method Based Entropy' Electronic Letters on Computer Vision and Image Analysis 5(1): pp. 30-40.
  31. Stollnitz, E. J. , DeRose, T. D. and Salesin D. H. May 1995 'Wavelets for computer graphics: A primer', part 1. IEEE Computer Graphics and Applications,15(3):pp. 76–84.
  32. Talukder, K. H. and Harada, K. , june 2006 'A Scheme of Wavelet Based Compression of 2D Image', Proc. IMECS, Hong Kong, pp. 531-53.
  33. Talukder, K. H. , Koichi Harada, K. 2007 ' Haar Wavelet Based Approach for Image Compression and Quality Assessment of Compressed Image' IAENG International Journal of Applied Mathematics, Volume 36, Issue 1.
  34. Devore, R. , Jawerth, B. and Lucier, B. March 1992 'Image compression through wavelet transform coding'. IEEE Transactions on Information Theory, 38(2):719–746.
  35. Marcellin, M. W. , Gormish, M. J. , Bilgin, A. and Boliek, M. P. , March 2000 'An overview of JPEG-2000' in Data Compression Conference Proceedings, pp. 523-541.
  36. Santa-Cruz, D. , Grobois, R. and Elibrahimi, T. , 2002 'JPEG 2000 performance evaluation and assessment' Signal Processing: Image Communication 17 (2002) 113–130 elsevier.
Index Terms

Computer Science
Information Sciences

Keywords

Digital image image compression agent multi-agent system (MAS)