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

Created by W.Langdon from gp-bibliography.bib Revision:1.8204

@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--16:25",
• month = may,
• year = "2017",
• keywords = "genetic algorithms, genetic programming, ANN, DT, perceived quality estimation, audiovisual quality dataset, MOS, no-reference models, machine learning",
• 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",
• size = "25 pages",
• 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