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Fractal Image Compression Using Dynamically Pipelined GPU Clusters

  • Munesh Singh Chauhan
  • Ashish Negi
  • Prashant Singh RanaEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 236)

Abstract

The main advantage of image compression is the rapid transmission of data. The conventional compression techniques exploit redundancy in images that can be encoded. The main idea is to remove redundancies when the image is to be stored and replace it back when the image is reconstructed. But the compression ratio of this technique is quite insignificant, and hence is not a suitable candidate for an efficient encoding technique. Other methods involve removing high frequency Fourier coefficients and retaining low frequency ones. This method uses discrete cosine transforms(DCT) and is used extensively in different flavors pertaining to the JPEG standards. Fractal compression provides resolution-independent encoding based on the contractive function concept. This concept is implemented using attractors (seed) that are encoded/copied using affine transformations of the plane. This transformation allows operations such as, skew, rotate, scale, and translate an input image which is in turn is extremely difficult or impossible to perform in JPEG images without having the problem of pixelization. Further, while decoding the fractal image, there exist no natural size, and thus the decoded image can be scaled to any output size without losing on the detail. A few years back fractal image was a purely a mathematical concept but with availability of cheap computing power like graphical processor units (GPUs) from Nvidia Corporation its realization is now possible graphically. The fractal compression is implemented using MatLab programming interface that runs on GPU clusters. The GPUs consist of many cores that together give a very high computing speed of over 24 GFLOPS. The advantage of fractal compression can have varied usage in satellite surveillance and reconnaissance, medical imaging, meteorology, oceanography, flight simulators, extra-terrestrial planets terrain mapping, aircraft body frame design and testing, film, gaming and animation media, and besides many other allied areas.

Keywords

GPU Fractal Attractor Affine transformation  PIFS 

References

  1. 1.
    Ahmed, N., Natarajan, T.: Discrete cosine transform. IEEE Trans. Comput. 23, 90–93 (1974)CrossRefzbMATHMathSciNetGoogle Scholar
  2. 2.
    An Introduction to Fractal Image Compression. Literature Number: BPRA065, Texas Instruments Europe, October 1997.Google Scholar
  3. 3.
    Barnsley, M.F., Sloan, A.: Chaotic compression. Comput. Graphics. World 5, 107–108 (1987)Google Scholar
  4. 4.
    Chady M.: Application of the Bulk Synchronous Parallel Model in Fractal Image Compression. School of Computer, Science (2004).Google Scholar
  5. 5.
    Erra, U.: Toward real time fractal image compression using graphics hardware. In: Proceedings of Advances in Visual Computing, LNCS, vol. 3804, Springer, pp. 723–728. 2005, doi: 10.1007/11595755_92.
  6. 6.
    Galabov, M.: Fractal image compression. In: International Conference on Computer System and Technologies - CompSysTech’2003 (1990).Google Scholar
  7. 7.
    Hockney, R.W.: Performance parameters and benchmarking of supercomputers. Parallel Comput. 17, 1111–1130 (1991)CrossRefGoogle Scholar
  8. 8.
    Hurtgen, B., Mols, P., Simon, S.F.: Fractal transform coding of color images. SPIE Conference on Visual Communications and Image Processing, Chicago, In (1994)Google Scholar
  9. 9.
    Jackson, D.J., Blom, T.: Fractal image compression using a circulating pipeline computation model. Int. J. Comput. Appl. (in review).Google Scholar
  10. 10.
    Jacquin, A.: Image coding based on a fractal theory of iterated contractive image transforms. In: SPIE, Visual Communications and Image Processing, vol. 1360 (1990).Google Scholar
  11. 11.
    Kominek, J.: Advances in fractal compression for multimedia applications. Multimedia Systems. Springer, New York (1997).Google Scholar
  12. 12.
    Line Transmission of Non-Telephone Signals. Video CODEC for AudioVisual Services, ITU-T Recommendation (H.261).Google Scholar
  13. 13.
    Mandelbrot, B.B.: The Fractal Geometry of Nature. W.H. Freeman and Company, San Francisco (1982). ISBN 0-7167-1186-9zbMATHGoogle Scholar
  14. 14.
    Monro, D.M., Wooley, S.J.: Fractal image compression without searching. In: Proceedings of ICASSP, vol. 5, pp. 557–560 (1994).Google Scholar
  15. 15.
    MPEG-7 Whitepaper. Sonera MediLab, 13 October 2003.Google Scholar
  16. 16.
    Owens, J.D. et al.: A survey of general-purpose computation on graphics hardware. In: Eurographics 2005, State of the Art Reports, pp. 21–51, August 2005.Google Scholar
  17. 17.
    Ruhl, M., Hartenstein, H.: Optimal fractal coding is NP-hard. Proceeding of IEEE Computer Society Washington, DC, USA, In (1997)Google Scholar
  18. 18.
    Series H: Audiovisual and multimedia systems, infrastructure of audiovisual services - Coding of moving video. Video coding for low bit rate communication ITU-T, ITU-T Recommendation (H.263).Google Scholar
  19. 19.
    Sodora, A.: Fractal Image Compression on the Graphics Card. Johns Hopkins University, Baltimore (2010)Google Scholar
  20. 20.
    Wallace, G.K.: The JPEG still picture compression standard. IEEE Trans. Consum. Electron. 38(1), 18–34 (1992)CrossRefGoogle Scholar

Copyright information

© Springer India 2014

Authors and Affiliations

  • Munesh Singh Chauhan
    • 1
  • Ashish Negi
    • 2
  • Prashant Singh Rana
    • 3
    Email author
  1. 1.Research ScholarPacific UniversityUdaipurIndia
  2. 2.Department of CSEG.B. Pant Engineering CollegeUttarakhandIndia
  3. 3.Research Scholar, Department of ICTIIITMGwaliorIndia

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