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

Created by W.Langdon from gp-bibliography.bib Revision:1.7642

@Article{Staelens:2013:ieeeCSVT,

• 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 Technology",
• 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 bitstream.",
• notes = "Also known as \cite{6422370}",

}

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

Citations