- 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.",
