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Weighted Feature Similarity – A Nonlinear Combination of Gradient and Phase Congruency for Full-Reference Image Quality Assessment

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Image Processing and Communications Challenges 4

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 184))

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Summary

In the paper the modified Feature Similarity metric has been discussed which is based on the nonlinear combination of two elements being the basics of the recently developed Feature Similarity metric for full-reference image quality assessment. Nevertheless, the influence of the gradient magnitude and phase congruency, used as two main elements of the metric, on the perceived quality is not necessarily equal. For this reason some experiments have been conducted in order to propose the weighting coefficients, applied as the local exponents, increasing the rank order correlation coefficients with subjective quality evaluations. The verification of the obtained results has been conducted using 5 ”state-of-the-art” benchmark databases and the obtained weighted FSIM metric’s performance results are better for all of them.

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Authors and Affiliations

  1. Faculty of Electrical Engineering, Department of Signal Processing and Multimedia Engineering, West Pomeranian University of Technology, Szczecin, 26. Kwietnia 10, 71-126, Szczecin, Poland

    Krzysztof Okarma

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  1. Krzysztof Okarma
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Correspondence to Krzysztof Okarma .

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Editors and Affiliations

  1. , Institute of Telecommunications, University of Technology & Life Sciences, ul.S.Kaliskiego 7, Bydgoszcz, 85-791, Poland

    Ryszard S. Choraś

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Okarma, K. (2013). Weighted Feature Similarity – A Nonlinear Combination of Gradient and Phase Congruency for Full-Reference Image Quality Assessment. In: Choraś, R. (eds) Image Processing and Communications Challenges 4. Advances in Intelligent Systems and Computing, vol 184. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32384-3_23

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  • DOI: https://doi.org/10.1007/978-3-642-32384-3_23

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-32383-6

  • Online ISBN: 978-3-642-32384-3

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