Video Rendering: Zooming Video Using Fractals

  • Maurizio Murroni
  • Giulio Soro
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3893)


Slow motion replay and spatial zooming are special effects used in video rendering. Already consolidated as commercial features of analog video players, today both these effects are likely to be extended to the digital environment. Purpose of this paper is to present a technique combining fractals (IFS) and wavelets to obtain a subjectively pleasant zoom and slow motion of digital video sequences. Active scene detection and post processing techniques are used to reduce computational cost and improve visual quality respectively. This study shows that the proposed technique produces better results than the state of the art techniques based either on data replication or classical interpolation.


Video Quality Visual Quality Slow Motion Iterate Function System Error Block 
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  1. 1.
    Thomas, G.A.: Distorting the time axis: motion compensated image processing in the studio. In: IBC 1988 IEE Conference Publication no 293, pp. 256–25 (1988)Google Scholar
  2. 2.
    Thomas, G.A., Lau, H.Y.K.: Generation of high quality slow motion replay using motion compensation. In: Conference Proceeding, IBC, pp. 121–125 (1990)Google Scholar
  3. 3.
    Barnsley, S., Demko, M.F.: Iterated function systems and the global construction of fractal. In: Proc. Royal Soc. London, vc A399, pp. 243–275 (1985)Google Scholar
  4. 4.
    Jaquin, A.E.: Image coding based on a fractal theory of iterated contractive image trasformation. IEEE Trans. on Image Processing 1(1), 18–30 (1992)CrossRefGoogle Scholar
  5. 5.
    Polvere, M., Nappi, M.: Speed-Up in Fractal Image Coding: Comparison of Methods. IEEE Trans. on Image Processing 9(6), 1002–1009 (2000)CrossRefGoogle Scholar
  6. 6.
    Reusens, E.: Overlapped Adaptive Partitioning for Image Coding Based on Theory of Iterated Function Systems. In: Proc. IEEE ICASSP, Adelaide, Australia, vol. 5, pp. V/569–V/572 (1994)Google Scholar
  7. 7.
    Barthel, K.U., Voye, T.: Three-Dimensional Fractal Video Coding. In: Proc. IEEE ICIP 1995, Washington, D.C, vol. III, pp. 260–263 (1995)Google Scholar
  8. 8.
    Barthel, K.U., Voye, T., Ruhl, G.: Combining Wavelet and Fractal Coding for 3-D Video Coding. In: Proc. IEEE ICIP 1996 Lausanne, vol. 1, pp. 181–185 (1996)Google Scholar
  9. 9.
    Ancis, M., Giusto, D.D.: Image data compression by adaptive vector quantization of classified wavelet coefficients. In: Proc. IEEE PACRIM Conference Victoria, pp. 330–333. Canada (1997)Google Scholar
  10. 10.
    Polidori, E., Dugelay, J.-L.: Zooming using Iterated Function Systems. In: NATO ASI Conference on Fractal Image Encoding and Analysis, Trondheim (1995)Google Scholar
  11. 11.
    ANSI T1.801.03 – 1996: American National Standard for Telecommunications – Digital Transport of One – Way Video Signals – Parameters for Objective Performance Assessment. Alliance for Telecommunications Industry Solutions, 1200 G Street, N. W., Suite 500, Washington, D.CGoogle Scholar
  12. 12.
    ITU-T Recommendation J.144R: Objective perceptual video quality measurement techniques for digital cable television in the presence of a full referenceGoogle Scholar
  13. 13.
    ITU-R Recommendation BT.1683: Objective perceptual video quality measurement techniques for standard definition digital broadcast television in the presence of a full referenceGoogle Scholar
  14. 14.
    Video Quality Research project,

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Maurizio Murroni
    • 1
  • Giulio Soro
    • 1
  1. 1.Department of Electrical and Electronic EngineeringUniversity of Cagliari, P.zza d’ ArmiCagliariItaly

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