Skip to main content
  • Research Article
  • Open access
  • Published:

H.264/AVC Video Compressed Traces: Multifractal and Fractal Analysis

Abstract

Publicly available long video traces encoded according to H.264/AVC were analyzed from the fractal and multifractal points of view. It was shown that such video traces, as compressed videos (H.261, H.263, and MPEG-4 Version 2) exhibit inherent long-range dependency, that is, fractal, property. Moreover they have high bit rate variability, particularly at higher compression ratios. Such signals may be better characterized by multifractal (MF) analysis, since this approach describes both local and global features of the process. From multifractal spectra of the frame size video traces it was shown that higher compression ratio produces broader and less regular MF spectra, indicating to higher MF nature and the existence of additive components in video traces. Considering individual frames (I, P, and B) and their MF spectra one can approve additive nature of compressed video and the particular influence of these frames to a whole MF spectrum. Since compressed video occupies a main part of transmission bandwidth, results obtained from MF analysis of compressed video may contribute to more accurate modeling of modern teletraffic. Moreover, by appropriate choice of the method for estimating MF quantities, an inverse MF analysis is possible, that means, from a once derived MF spectrum of observed signal it is possible to recognize and extract parts of the signal which are characterized by particular values of multifractal parameters. Intensive simulations and results obtained confirm the applicability and efficiency of MF analysis of compressed video.

References

  1. Rao K, Bojkovic Z, Milovanovic D: Multimedia Communication Systems: Techniques, Standards, and Networks. Prentice-Hall, Englewood Cliffs, NJ, USA; 2002.

    Google Scholar 

  2. Wang Y, Osterman J, Zhang YQ: Video Processing and Communications. Prentice-Hall, Englewood Cliffs, NJ, USA; 2002.

    Google Scholar 

  3. Schäfer R, Wiegand T, Schwarz H: The emerging H.264/AVC standard. EBU Technical Review 2003., (293):

  4. ITU-T H.263 Recommendation, ITU-T, Geneva, Switzerland, 2000

  5. Draft ITU-T Rec. H.264, ISO/IEC 14496-10, 2002 E

  6. Garrett M: Contributions toward real-time services on packet switched networks, M.S. thesis. Columbia University, New York, NY, USA; 1993.

    Google Scholar 

  7. Riedi R, Vehel JL: Multifractal properties of TCP traffic: a numerical study. In INRIA Research Report 3129. INRIA, Rocquencourt, Le Chesnay Cedex, France; 1997. https://doi.org/www.stat.rice.edu/%7Eriedi/cv_publications.html

    Google Scholar 

  8. Fitzek F, Reisslein M: MPEG-4 and H.263 video traces for network performance evaluation. In TKN Technical Report TKN-00-06. Technical University Berlin, Berlin, Germany; 2000.

    Google Scholar 

  9. Reisslein M, Lassetter J, Ratnam S, Lotfallah O, Fitzek F, Panchanathan S: Traffic and quality characterization of scalable encoded video: a large-scale trace-based study, part 1: overview and definitions. Telecommunications Research Center, Department of Electrical Engineering, Arizona State University, Tempe, Ariz, USA; 2002.https://doi.org/peach.eas.asu.edu/index.html

    Google Scholar 

  10. Reisslein M, Lassetter J, Ratnam S, Lotfallah O, Fitzek F, Panchanathan S: Traffic and quality characterization of scalable encoded video: a large-scale trace-based study, part 2: statistical analysis of single-layer encoded video. Telecommunications Research Center, Department of Electrical Engineering, Arizona State University, Tempe, Ariz, USA; 2002.https://doi.org/www.eas.asu.edu/trace

    Google Scholar 

  11. Reisslein M, Lassetter J, Ratnam S, Lotfallah O, Fitzek F, Panchanathan S: Traffic and quality characterization of scalable encoded video: a large-scale trace-based study, part 3: statistical analysis of temporal scalable encoded video. Telecommunications Research Center, Department of Electrical Engineering, Arizona State University, Tempe, Ariz, USA; 2002.https://doi.org/www.eas.asu.edu/trace

    Google Scholar 

  12. Fitzek F, Zorzi M, Seeling P, Reisslein M: Video and audio trace files of pre-encoded video content for network performance measurements. Telecommunications Research Center, Department of Electrical Engineering, Arizona State University, Tempe, Ariz, USA; 2003.https://doi.org/www.eas.asu.edu/trace

    Google Scholar 

  13. Krishna M, Gadre V, Desai U: Multifractal Based Network Traffic Modeling. Kluwer Academic Press, Boston, Mass, USA; 2003.

    Google Scholar 

  14. https://doi.org/www.acticom.de

  15. Reljin I: Neural network based cell scheduling in ATM node. IEEE Communications Letters 1998, 2(3):78–80. 10.1109/4234.662633

    Article  Google Scholar 

  16. Reljin I, Reljin B: Neurocomputing in teletraffic: multifractal spectrum approximation. Proceedings of the 5th Seminar on Neural Network Applications in Electrical Engineering (NEUREL '00), September 2000, Belgrade, Yugoslavia 24–31.

    Google Scholar 

  17. Reljin B, Reljin I: Multimedia: the impact on the teletraffic. In Book 2. Edited by: Mastorakis N. World Scientific and Engineering Society Press, Clearance Center, Danvers, Mass, USA; 2000:366–373.

    Google Scholar 

  18. Reljin I, Reljin B: Statistical and multifractal characteristics of H.263 compressed video streams. In Proceedings of 20th Symp. on New Technologies in Post and Telecomm. Traffic, December 2002, Belgrade, Yugoslavia. Faculty of Traffic Eng.; 193–205.

    Google Scholar 

  19. Reljin I, Reljin B: Fractal and multifractal analyses of compressed video sequences. Facta Universitatis (NIS) Series: Electronics and Energetics 2003, 16(3):401–414. 10.2298/FUEE0303401R

    MATH  Google Scholar 

  20. Taqqu M, Teverovsky V, Willinger W: Estimators for long-range dependence: an empirical study. Fractals 1995, 3(4):785–788. 10.1142/S0218348X95000692

    Article  Google Scholar 

  21. Roughan M, Veitch D, Abry P: On-line estimation of the parameters of long-range dependence. Proceedings of IEEE Global Telecommunications Conference (GLOBECOM '98), November 1998, Sydney, NSW, Australia 6: 3716–3721.

    Google Scholar 

  22. Beran I: Statistics for Long-Memory Processes. Chapman & Hall, New York, NY, USA; 1994.

    MATH  Google Scholar 

  23. Reljin I: A neural network control of ATM multiplexer, M.S. thesis. Faculty of Electrical Engineering, University of Belgrade, Belgrade, Yugoslavia; 1998.

    Google Scholar 

  24. Reljin B, Reljin I: Neural networks in teletraffic control: pro et contra? Proceedings of the 4th International Conference on Telecommunications in Modern Satellite, Cable and Broadcasting Services (TELSIKS '99), October 1999, Niš, Yugoslavia 2: 518–527.

    Article  Google Scholar 

  25. Mandelbrot B: The Fractal Geometry of Nature. W. H. Freeman, New York, NY, USA; 1983.

    Book  Google Scholar 

  26. Peitgen H, Jurgens H, Saupe D: Chaos and Fractals. Springer, New York, NY, USA; 1992.

    Book  Google Scholar 

  27. Turner M, Blackledge J, Andrews P: Fractal Geometry in Digital Imaging. Academic Press, New York, NY, USA; 1998.

    Google Scholar 

  28. Evertsz C, Mandelbrot B: Multifractal measures. Appendix B. In Chaos and Fractals. Edited by: Peitgen H, Jurgens H, Saupe D. Springer, New York, NY, USA; 1992:849–881.

    Google Scholar 

  29. Iannaccone P, Khokha M (Eds): Fractal Geometry in Biological Systems. CRC Press, Boca Raton, Fla, USA; 1996.

    Google Scholar 

  30. Véhel JL, Mignot P: Multifractal segmentation of images. Fractals 1994, 2(3):371–377. 10.1142/S0218348X94000466

    Article  Google Scholar 

  31. Véhel JL: Introduction to the multufractal analysis of images. INRIA, Rocquencourt, Le Chesnay Cedex, France; 1996.

    Google Scholar 

  32. Reljin I, Reljin B: Fractal geometry and multifractals in analyzing and processing medical data and images. Archive of Oncology 2002, 10(4):283–293. 10.2298/AOO0204283R

    Article  Google Scholar 

  33. Stojic T, Reljin I, Reljin B: Adaptation of multifractal analysis to segmentation of microcalcifications in digital mammograms. Physica A: Statistical Mechanics and its Applications 2006, 367: 494–508.

    Article  Google Scholar 

  34. Chhabra A, Jensen R:Direct determination of the f() singularity spectrum. Physical Review Letters 1989, 62(12):1327–1330. 10.1103/PhysRevLett.62.1327

    Article  MathSciNet  Google Scholar 

  35. Gammel BMATPACK Library Release 1.4, https://doi.org/users.physik.tu-muenchen.de/gammel/matpack/html/LibDoc/Tools/install.html

  36. FracLab v1.1, 2003, https://doi.org/www.irccyn.ec-nantes.fr/hebergement/FracLab

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Irini Reljin.

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License ( https://creativecommons.org/licenses/by/2.0 ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Reprints and permissions

About this article

Cite this article

Reljin, I., Samčović, A. & Reljin, B. H.264/AVC Video Compressed Traces: Multifractal and Fractal Analysis. EURASIP J. Adv. Signal Process. 2006, 075217 (2006). https://doi.org/10.1155/ASP/2006/75217

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1155/ASP/2006/75217

Keywords