A non-stationary hidden Markov model of multiview video traffic

Lorenzo Rossi, Jacob Chakareski, Pascal Frossard, Stefania Colonnese

Research output: Chapter in Book/Report/Conference proceedingConference contribution

9 Scopus citations

Abstract

Multiview video is increasingly getting attention due to emerging applications such as 3DTV and immersive teleconferencing. In this paper, we present a non-stationary Hidden Markov Model (HMM) for characterizing the data rate of compressed multiview content. The states of the model correspond to different video activity levels and exhibit a Poisson state duration distribution. We derive a stable maximum likelihood algorithm for estimating the parameters of our multiview traffic model. Synthetic data generated by the model exhibits statistics that closely match those of actual multiview data. In addition, we demonstrate the high accuracy of the model in two multiview streaming applications by evaluating the frame loss rate of a constrained network buffer fed by actual and synthetic data.

Original languageEnglish (US)
Title of host publication2010 IEEE International Conference on Image Processing, ICIP 2010 - Proceedings
Pages2921-2924
Number of pages4
DOIs
StatePublished - 2010
Externally publishedYes
Event2010 17th IEEE International Conference on Image Processing, ICIP 2010 - Hong Kong, Hong Kong
Duration: Sep 26 2010Sep 29 2010

Publication series

NameProceedings - International Conference on Image Processing, ICIP
ISSN (Print)1522-4880

Conference

Conference2010 17th IEEE International Conference on Image Processing, ICIP 2010
Country/TerritoryHong Kong
CityHong Kong
Period9/26/109/29/10

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

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