Advertisement

Network-Diffused Media Scaling for Multimedia Content Services

  • Omid E. Kia
  • Jaakko J. Sauvola
  • David S. Doermann
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1718)

Abstract

In this paper we propose a new approach to adaptation of content-based processing, media preparation and presentation. We address multimedia consumption terminals having a variable amount of resources. The proposed technique adapts the required service data contained within the multimedia to the abilities of the hybrid network elements and application requirements. In our approach the data representations are transformed and converted efficiently to reduce delivery requirements and to emphasize quality of service ratio. An experimental system is implemented in a distributed environment providing only low processing requirements from the data processing nodes. We perform media profiling based on cross-media translation to achieve an efficient scaling and fit the content with terminal capabilities. Our approach aims to build an intelligent content service infrastructure in a way that the servers, nodes, and terminals are aware of their capabilities along with the capabilities of their surroundings. We demonstrate with an example service with scalable multimedia delivery over hybrid network elements.

Keywords

Document Image Multimedia Service Cache Size Effective Bandwidth Media Object 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

7 References

  1. 1.
    International Telecommunications Union (1998) H.323 standards specification, version 2.0.Google Scholar
  2. 2.
    M. Palola, R. Kaksonen, M. Heikkinen, S. Kaukonen, T. Ojala, T. Tervo & J. Sauvola (1998) CTI State-of-the-art. A technical report, CTI project, 64 pages.Google Scholar
  3. 3.
    Armando Fox, Steven D. Gribble, Eric A. Brewer, and Elan Amir. Adapting to network and client variability via on-demand dynamic distillation. In Proc. Seventh Intl. Conf. on Arch. Support for Prog. Lang. and Oper. Sys. (ASPLOS-VII), 1997.Google Scholar
  4. 4.
    Armando Fox, Steven D. Gribble, Yatin Chawathe, and Eric A. Brewer. Cluster-based scalable network services. In Proc. 1997 Symposium on Operating Systems Principles (SOSP-16), St-Malo, France, 1997.Google Scholar
  5. 5.
    R. Gallager. Variations on a theme by Huffman. IEEE Transactions on Information Theory, 24:668–674, 1978.zbMATHCrossRefGoogle Scholar
  6. 6.
    A. Gersho and R. Gray. Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston, 1992.zbMATHGoogle Scholar
  7. 7.
    A. Jain. Fundamentals of Digital Image Processing. Prentice-Hall, 1989.Google Scholar
  8. 8.
    O. Kia. Document Image Compression and Analysis. PhD Thesis, University of Maryland at College Park, 1997.Google Scholar
  9. 9.
    D. Knuth. Optimal binary search trees. Acta Informatica, 1:14–25, 1971.zbMATHCrossRefGoogle Scholar
  10. 10.
    J. Li and C.C.J. Kuo. Progressive Coding of 3-D Graphic Models. IEEE Proceedings, 86(6), pp 1052–1063, 1998.CrossRefGoogle Scholar
  11. 11.
    S. McCanne. Scalable Compression and Transmission of Internet Multicast Video. PhD Thesis, University of California, Berkeley, 1996.Google Scholar
  12. 12.
    J. Sauvola. Document Analysis Techniques and System Components with Applications in Image Retrieval. PhD Thesis, University of Oulu, Finland, 1997.Google Scholar
  13. 13.
    K. Sayood. Introduction to data compression, Morgan Kaufman Publishers, 1996.Google Scholar
  14. 14.
    J. Vitter. Design and analysis of dynamic Huffman codes. Journal of the Association for Computing Machinary, 34:825–845, 1987.zbMATHGoogle Scholar
  15. 15.
    J. Ziv and A. Lempel. A universal algorithm for sequential data compression. IEEE Transactions on Information Theory, 23:337–343, 1977.zbMATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1999

Authors and Affiliations

  • Omid E. Kia
    • 1
  • Jaakko J. Sauvola
    • 2
  • David S. Doermann
    • 3
  1. 1.Information Technology LaboratoryNational Institute of Standards and TechnologyGaithersburg
  2. 2.Machine Vision and Media Processing GroupInfotech Oulu University of OuluOuluFinland
  3. 3.Language and Media Processing LaboratoryInstitute for Advanced Computing Studies University of MarylandCollege park

Personalised recommendations