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A Mixture of Experts Approach to Multi-strategy Image Quality Assessment

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Image Analysis and Recognition (ICIAR 2012)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 7324))

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Abstract

The success of some recently proposed multi-strategy image quality metrics supports the hypothesis that the Human Visual System (HVS) uses multiple strategies when assessing image quality, where the effect from each strategy on the final quality prediction is conditioned on the quality level of the test image. To date, how to optimally combine multiple strategies into a final quality prediction remains an unsolved problem, especially when more than two strategies are involved. In this paper, we present a data-driven combination method based on a conditional Bayesian Mixture of Experts (BME) model. This method provides an effective way to model the interaction of a flexible number of strategies. Extensive evaluation on three publicly-available image quality databases demonstrates the potential of our method.

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Author information

Authors and Affiliations

  1. School of Computing Science, Simon Fraser University, 8888 University Drive, Burnaby, B.C., Canada, V5A 1S6

    Peng Peng & Ze-Nian Li

Authors
  1. Peng Peng
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  2. Ze-Nian Li
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Editor information

Editors and Affiliations

  1. Faculty of Engineering, Institute of Biomedical Engineering, University of Porto, Rua Dr. Roberto Frias, 4200-465, Porto, Portugal

    Aurélio Campilho

  2. Department of Electrical and Computer Engineering, University of Waterloo, N2L 3G1, Waterloo, ON, Canada

    Mohamed Kamel

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© 2012 Springer-Verlag Berlin Heidelberg

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Peng, P., Li, ZN. (2012). A Mixture of Experts Approach to Multi-strategy Image Quality Assessment. In: Campilho, A., Kamel, M. (eds) Image Analysis and Recognition. ICIAR 2012. Lecture Notes in Computer Science, vol 7324. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31295-3_15

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  • DOI: https://doi.org/10.1007/978-3-642-31295-3_15

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-31294-6

  • Online ISBN: 978-3-642-31295-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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