Perceived Audiovisual Quality Modelling based on Decison Trees, Genetic Programming and Neural Networks

Created by W.Langdon from gp-bibliography.bib Revision:1.4448

@Misc{journals/corr/abs-1801-05889,
  author =       "Edip Demirbilek and Jean-Charles Gregoire",
  title =        "Perceived Audiovisual Quality Modelling based on
                 Decison Trees, Genetic Programming and Neural
                 Networks",
  howpublished = "arXiv",
  year =         "2017",
  month =        "6 " # dec,
  keywords =     "genetic algorithms, genetic programming, perceived
                 quality, audiovisual dataset, bitstream model, machine
                 learning",
  URL =          "http://arxiv.org/abs/1801.05889",
  size =         "14 pages",
  abstract =     "Our objective is to build machine learning based
                 models that predict audiovisual quality directly from a
                 set of correlated parameters that are extracted from a
                 target quality dataset. We have used the bitstream
                 version of the INRS audiovisual quality dataset that
                 reflects contemporary real-time configurations for
                 video frame rate, video quantization, noise reduction
                 parameters and network packet loss rate. We have used
                 this dataset to build bitstream perceived quality
                 estimation models based on the Random Forests, Bagging,
                 Deep Learning and Genetic Programming methods.

                 We have taken an empirical approach and have generated
                 models varying from very simple to the most complex
                 depending on the number of features used from the
                 quality dataset. Random Forests and Bagging models have
                 overall generated the most accurate results in terms of
                 RMSE and Pearson correlation coefficient values. Deep
                 Learning and Genetic Programming based bitstream models
                 have also achieved good results but that high
                 performance was observed only with a limited range of
                 features. We have also obtained the epsilon-insensitive
                 RMSE values for each model and have computed the
                 significance of the difference between the correlation
                 coefficients.

                 Overall we conclude that computing the bitstream
                 information is worth the effort it takes to generate
                 and helps to build more accurate models for real-time
                 communications. However, it is useful only for the
                 deployment of the right algorithms with the carefully
                 selected subset of the features. The dataset and tools
                 that have been developed during this research are
                 publicly available for research and development
                 purposes.",
}

Genetic Programming entries for Edip Demirbilek Jean-Charles Gregoire

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