Constructing a No-Reference H.264/AVC Bitstream-based Video Quality Metric using Genetic Programming-based Symbolic Regression

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  author =       "Nicolas Staelens and Dirk Deschrijver and 
                 Ekaterina Vladislavleva and Brecht Vermeulen and Tom Dhaene and 
                 Piet Demeester",
  journal =      "IEEE Transactions on Circuits and Systems for Video
  title =        "Constructing a No-Reference {H.264/AVC}
                 Bitstream-based Video Quality Metric using Genetic
                 Programming-based Symbolic Regression",
  year =         "2013",
  volume =       "23",
  number =       "8",
  pages =        "1322--1333",
  keywords =     "genetic algorithms, genetic programming, H.264/AVC,
                 High Definition, No-Reference, Objective video quality
                 metric, Quality of Experience (QoE)",
  DOI =          "doi:10.1109/TCSVT.2013.2243052",
  ISSN =         "1051-8215",
  abstract =     "In order to ensure optimal Quality of Experience
                 towards the end-users during video streaming, automatic
                 video quality assessment becomes an important
                 field-of-interest to video service providers. Objective
                 video quality metrics try to estimate perceived quality
                 with a high accuracy and in an automated manner. In
                 traditional approaches, these metrics model the complex
                 properties of the Human Visual System. More recently,
                 however, it has been shown that Machine Learning
                 approaches can also yield competitive results. In this
                 article, we present a novel No-Reference
                 bitstream-based objective video quality metric that is
                 constructed by Genetic Programming-based Symbolic
                 Regression. A key benefit of this approach is that it
                 calculates reliable white-box models that allow us to
                 determine the importance of the parameters.
                 Additionally, these models can provide human insight
                 into the underlying principles of subjective video
                 quality assessment. Numerical results show that
                 perceived quality can be modelled with a high accuracy
                 using only parameters extracted from the received video
  notes =        "Also known as \cite{6422370}",

Genetic Programming entries for Nicolas Staelens Dirk Deschrijver Ekaterina (Katya) Vladislavleva Brecht Vermeulen Tom Dhaene Piet Demeester