Machine learning based reduced reference bitstream audiovisual quality prediction models for realtime communications
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- @InProceedings{Demirbilek:2017:ieeeICME,
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author = "Edip Demirbilek and Jean-Charles Gregoire",
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booktitle = "2017 IEEE International Conference on Multimedia and
Expo (ICME)",
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title = "Machine learning based reduced reference bitstream
audiovisual quality prediction models for realtime
communications",
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year = "2017",
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pages = "571--576",
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abstract = "Perceived quality prediction models for multimedia
services vary greatly depending on the type of the data
and on the amount of information related to the
original signal used. In this research, we have
developed machine learning-based reduced-reference
bitstream audiovisual quality prediction models by
using the parametric version of the publicly available
INRS audiovisual quality dataset. As that original INRS
dataset did not contain bitstream information but
provided both reference and transmitted videos, we have
computed its bitstream version to develop the
reduced-reference bitstream models. We have compared
the performance of the Decision Trees based ensemble
methods, Genetic Programming and Deep Learning models
on this bitstream version of the dataset and have also
compared these results with the results of the
no-reference parametric models on the parametric
version of the dataset. Decision Trees based ensemble
methods outperformed Deep Learning and Genetic
Programming based models when reduced-reference
bitstream data was used and outperformed all existing
no-reference parametric models that were trained and
tested on the parametric version of the dataset. Our
studies show that Decision Trees based approaches are
well suited for no-reference parametric models as well
as for reduced-reference bitstream models.",
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keywords = "genetic algorithms, genetic programming",
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DOI = "doi:10.1109/ICME.2017.8019462",
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month = jul,
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notes = "Also known as \cite{8019462}",
- }
Genetic Programming entries for
Edip Demirbilek
Jean-Charles Gregoire
Citations