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Journal of Scheduling

, Volume 7, Issue 2, pp 105–117 | Cite as

Quality Control for Scalable Media Processing Applications

  • Clemens C. WüstEmail author
  • Wim F.J. Verhaegh
Article

Abstract

Many media processing applications create a load that varies significantly over time. Hence, if such an application is assigned a lower processing-time budget than needed in its worst-case load situation, deadline misses are likely to occur. This problem can be dealt with by designing media processing applications in a scalable fashion. A scalable media processing application can run in multiple qualities, leading to correspondingly different resource demands. The problem we consider is to find an accompanying quality control strategy, which minimizes both the number of deadline misses and the number of quality changes, while maximizing the quality of processing. We present an initial approach to the above problem by modeling it as a Markov decision process (MDP). Our model is based on measuring relative progress at milestones. Solving the MDP results in a quality control strategy that can be applied during runtime with only little overhead. We evaluate our approach by means of a practical example, which concerns a scalable MPEG-2 decoder.

media processing real-time systems load variations scalability quality control Markov decision process 

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REFERENCES

  1. Bril, R. J., M. Gabrani, C. Hentschel, G. C. van Loo, and E. F. M. Steffens, “QoS for consumer terminals and its support for product families,” in Proc. International Conference on Media Futures (ICMF), Florence, Italy, May 2001a, pp. 299–302.Google Scholar
  2. Bril, R. J., C. Hentschel, E. F. M. Steffens, M. Gabrani, G. C. van Loo, and J. H. A. Gelissen, “Multimedia QoS in consumer terminals,” in Proc. IEEE Workshop on Signal Processing Systems (SIPS), Antwerp, Belgium, September 2001b, pp. 332–343.Google Scholar
  3. Gelissen, J. H. A., “The ITEA project EUROPA, a software platform for digital CE appliances,” in Proc. International Conference on Media Futures (ICMF), Florence, Italy, May 2001, pp. 157–160.Google Scholar
  4. Hentschel, C., R. J. Bril, M. Gabrani, L. Steffens, K. van Zon, and S. van Loo, “Scalable video algorithms and dynamic resource management for consumer terminals,” in Proc. International Conference on Media Futures (ICMF), Florence, Italy, May 2001, pp. 193–196.Google Scholar
  5. Lan, T., Y. Chen, and Z. Zhong, “MPEG2 decoding complexity regulation for a media processor”, in Proc. Fourth IEEE Workshop on Multimedia Signal Processing (MMSP), Cannes, France, October 2001, pp. 193–198.Google Scholar
  6. Peng, S., “Complexity scalable video decoding via IDCT data pruning,” in Digest of Technical Papers IEEE International Conference on Consumer Electronics (ICCE), Los Angeles, CA, June 2001, pp. 74–75.Google Scholar
  7. Puterman, M. L., Markov Decision Processes: Discrete Stochastic Dynamic Programming, Wiley Series in Probability and Mathematical Statistics, Wiley-Interscience, New York, 1994.Google Scholar
  8. Slavenburg, G. A., S. Rathnam, and H. Dijkstra, “The TriMedia TM-1 PCI VLIW mediaprocessor,” in Proc. Eighth IEEE Symposium on High-Performance Chips, Hot Chips 8, Stanford, CA, August 1996, pp. 171–177.Google Scholar
  9. van der Wal, J., Stochastic Dynamic Programming, PhD thesis, Mathematisch Centrum, Amsterdam, The Netherlands, 1980.Google Scholar
  10. White, D. J., Markov Decision Processes, John Wiley & Sons Ltd., 1993.Google Scholar

Copyright information

© Kluwer Academic Publishers 2004

Authors and Affiliations

  1. 1.Philips Research LaboratoriesEindhovenThe Netherlands

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