Machine Learning-Based Parametric Audiovisual Quality Prediction Models for Real-Time Communications

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@Article{Demirbilek:2017:MLB,
  author =       "Edip Demirbilek and Jean-Charles Gregoire",
  title =        "Machine Learning-Based Parametric Audiovisual Quality
                 Prediction Models for Real-Time Communications",
  journal =      "ACM Transactions on Multimedia Computing,
                 Communications, and Applications",
  volume =       "13",
  number =       "2",
  pages =        "16:1--16:??",
  month =        may,
  year =         "2017",
  keywords =     "genetic algorithms, genetic programming",
  articleno =    "16",
  ISSN =         "1551-6857",
  bibdate =      "Fri Jun 16 14:48:38 MDT 2017",
  bibsource =    "http://www.acm.org/pubs/contents/journals/tomccap/;
                 http://www.math.utah.edu/pub/tex/bib/tomccap.bib",
  URL =          "http://portal.acm.org/browse_dl.cfm?idx=J961",
  DOI =          "doi:10.1145/3051482",
  abstract =     "In order to mechanically predict audiovisual quality
                 in interactive multimedia services, we have developed
                 machine learning--based no-reference parametric models.
                 We have compared Decision Trees--based ensemble
                 methods, Genetic Programming and Deep Learning models
                 that have one and more hidden layers. We have used the
                 Institut national de la recherche scientifique (INRS)
                 audiovisual quality dataset specifically designed to
                 include ranges of parameters and degradations typically
                 seen in real-time communications. Decision Trees, based
                 ensemble methods have outperformed both Deep Learning,
                 and Genetic Programming--based models in terms of
                 Root-Mean-Square Error (RMSE) and Pearson correlation
                 values. We have also trained and developed models on
                 various publicly available datasets and have compared
                 our results with those of these original models. Our
                 studies show that Random Forests--based prediction
                 models achieve high accuracy for both the INRS
                 audiovisual quality dataset and other publicly
                 available comparable datasets.",
  acknowledgement = "Nelson H. F. Beebe, University of Utah, Department
                 of Mathematics, 110 LCB, 155 S 1400 E RM 233, Salt Lake
                 City, UT 84112-0090, USA, Tel: +1 801 581 5254, FAX: +1
                 801 581 4148, e-mail: \path|beebe@math.utah.edu|,
                 \path|beebe@acm.org|, \path|beebe@computer.org|
                 (Internet), URL:
                 \path|http://www.math.utah.edu/~beebe/|",
}

Genetic Programming entries for Edip Demirbilek Jean-Charles Gregoire

Citations