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Constructing a no-reference H.264/AVC bitstream-based video quality metric using genetic programming-based symbolic regression

Nicolas Staelens (UGent) , Dirk Deschrijver (UGent) , E Vladislavleva, Brecht Vermeulen (UGent) , Tom Dhaene (UGent) and Piet Demeester (UGent)
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Abstract
In order to ensure optimal quality of experience toward 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 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 paper, 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 modeled with high accuracy using only parameters extracted from the received video bitstream.
Keywords
MPEG-2 VIDEO, IBCN, NEURAL-NETWORKS, VISIBILITY, H.264/AVC, high definition, no-reference, objective video quality metric, quality of experience (QoE)

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Citation

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MLA
Staelens, Nicolas et al. “Constructing a No-reference H.264/AVC Bitstream-based Video Quality Metric Using Genetic Programming-based Symbolic Regression.” IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 23.8 (2013): 1322–1333. Print.
APA
Staelens, N., Deschrijver, D., Vladislavleva, E., Vermeulen, B., Dhaene, T., & Demeester, P. (2013). Constructing a no-reference H.264/AVC bitstream-based video quality metric using genetic programming-based symbolic regression. IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, 23(8), 1322–1333.
Chicago author-date
Staelens, Nicolas, Dirk Deschrijver, E Vladislavleva, Brecht Vermeulen, Tom Dhaene, and Piet Demeester. 2013. “Constructing a No-reference H.264/AVC Bitstream-based Video Quality Metric Using Genetic Programming-based Symbolic Regression.” Ieee Transactions on Circuits and Systems for Video Technology 23 (8): 1322–1333.
Chicago author-date (all authors)
Staelens, Nicolas, Dirk Deschrijver, E Vladislavleva, Brecht Vermeulen, Tom Dhaene, and Piet Demeester. 2013. “Constructing a No-reference H.264/AVC Bitstream-based Video Quality Metric Using Genetic Programming-based Symbolic Regression.” Ieee Transactions on Circuits and Systems for Video Technology 23 (8): 1322–1333.
Vancouver
1.
Staelens N, Deschrijver D, Vladislavleva E, Vermeulen B, Dhaene T, Demeester P. Constructing a no-reference H.264/AVC bitstream-based video quality metric using genetic programming-based symbolic regression. IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY. 2013;23(8):1322–33.
IEEE
[1]
N. Staelens, D. Deschrijver, E. Vladislavleva, B. Vermeulen, T. Dhaene, and P. Demeester, “Constructing a no-reference H.264/AVC bitstream-based video quality metric using genetic programming-based symbolic regression,” IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, vol. 23, no. 8, pp. 1322–1333, 2013.
@article{4190529,
  abstract     = {In order to ensure optimal quality of experience toward 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 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 paper, 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 modeled with high accuracy using only parameters extracted from the received video bitstream.},
  author       = {Staelens, Nicolas and Deschrijver, Dirk and Vladislavleva, E and Vermeulen, Brecht and Dhaene, Tom and Demeester, Piet},
  issn         = {1051-8215},
  journal      = {IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY},
  keywords     = {MPEG-2 VIDEO,IBCN,NEURAL-NETWORKS,VISIBILITY,H.264/AVC,high definition,no-reference,objective video quality metric,quality of experience (QoE)},
  language     = {eng},
  number       = {8},
  pages        = {1322--1333},
  title        = {Constructing a no-reference H.264/AVC bitstream-based video quality metric using genetic programming-based symbolic regression},
  url          = {http://dx.doi.org/10.1109/TCSVT.2013.2243052},
  volume       = {23},
  year         = {2013},
}

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