Skip to main content

Evaluating Users’ Satisfaction in Packet Networks Using Random Neural Networks

  • Conference paper
Artificial Neural Networks – ICANN 2006 (ICANN 2006)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 4131))

Included in the following conference series:


Quantifying the quality of a video or audio transmission over the Internet is usually a hard task, as based on the statistical processing of the evaluations made by a panel of humans (the corresponding and standardized area is called subjective testing). In this paper we describe a methodology called Pseudo-Subjective Quality Assessment (PSQA), based on Random Neural Networks, which is able to perform this task automatically, accurately and efficiently. RNN had been chosen here because of their good performances over other possibilities; this is discussed in the paper. Some new insights on PSQA’s use and performance are also given. In particular we discuss new results concerning PSQA–based dynamic quality control, and conversational quality assessment.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others


  1. Gelenbe, E.: Random Neural Networks with Negative and Positive Signals and Product Form Solution. Neural Computation 1(4), 502–511 (1989)

    Article  Google Scholar 

  2. Gelenbe, E.: Stability of the Random Neural Network Model. In: Proc. of Neural Computation Workshop, Berlin, West Germany, February 1990, pp. 56–68 (1990)

    Google Scholar 

  3. Gelenbe, E.: Learning in the Recurrent Random Neural Network. Neural Computation 5(1), 154–511 (1993)

    Article  MathSciNet  Google Scholar 

  4. ITU-T Recommendation G.107. The E-model, a Computational Model for Use in Transmission Planning (March 2005),

  5. ITU-T Recommendation P.563. Single–ended Method for Objective Speech Quality Assessment in Narrow–band Telephony Applications (May 2004)

    Google Scholar 

  6. ITU-T Recommendation P.800. Methods for Subjective Determination of Transmission Quality (August 1996)

    Google Scholar 

  7. ITU-T Recommendation P.862. Perceptual Evaluation of Speech Quality (Pesq), an Objective Method for End-To-End Speech Quality Assessment of Narrowband Telephone Networks and Speech Codecs (2001)

    Google Scholar 

  8. ITU-T Recommendation P.920. Interactive test methods for audiovisual communications (2000)

    Google Scholar 

  9. Mohamed, S.: Automatic Evaluation of Real-Time Multimedia Quality: a Neural Network Approach. PhD thesis, INRIA/IRISA, Univ. Rennes I, Rennes, France (January 2003)

    Google Scholar 

  10. Mohamed, S., Rubino, G.: A Study of Real–time Packet Video Quality Using Random Neural Networks. IEEE Transactions On Circuits and Systems for Video Technology 12(12), 1071–1083 (2002)

    Article  Google Scholar 

  11. Rubino, G.: Quantifying the quality of audio and video transmissions over the internet: the psqa approach. In: Barria, J. (ed.) Design and Operations of Communication Networks: A Review of Wired and Wireless Modelling and Management Challenges, Imperial College Press, London (2005)

    Google Scholar 

  12. Rubino, G., Varela, M.: Evaluating the utility of media–dependent FEC in VoIP flows. In: Solé-Pareta, J., Smirnov, M., Van Mieghem, P., Domingo-Pascual, J., Monteiro, E., Reichl, P., Stiller, B., Gibbens, R.J. (eds.) QofIS 2004. LNCS, vol. 3266, pp. 31–43. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  13. Rubino, G., Varela, M., Mohamed, S.: Performance evaluation of real-time speech through a packet network: a random neural networks-based approach. Performance evaluation 57(2), 141–162 (2004)

    Article  Google Scholar 

  14. Rubino, G., Varela, M.: A new approach for the prediction of end-to-end performance of multimedia streams. In: Proceedings of the Measurement of Speech and Audio Quality in Networks workshop (MESAQIN 2004) (September 2004)

    Google Scholar 

Download references

Author information

Authors and Affiliations


Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2006 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Rubino, G., Tirilly, P., Varela, M. (2006). Evaluating Users’ Satisfaction in Packet Networks Using Random Neural Networks. In: Kollias, S.D., Stafylopatis, A., Duch, W., Oja, E. (eds) Artificial Neural Networks – ICANN 2006. ICANN 2006. Lecture Notes in Computer Science, vol 4131. Springer, Berlin, Heidelberg.

Download citation

  • DOI:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-38625-4

  • Online ISBN: 978-3-540-38627-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics