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Evolutionary Optimization of Tone Mapped Image Quality Index

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Artificial Evolution (EA 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10764))

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Abstract

The development of reliable image quality measures for the assessment of tone mapped images constitutes a significant advancement in high dynamic range imaging. The ability to objectively assess the quality of tone mapped images allows treating tone mapping as an optimization problem that can be solved by automated algorithms, without the need for human input. The most prominent quality measure for tone mapped images is the Tone Mapped Image Quality Index. An optimization approach has been proposed in connection with the introduction of that measure that operates in a high-dimensional search space and is computationally expensive. In this paper, we propose an evolutionary algorithm to solve the tone mapping problem using a generic tone mapping operator and the Tone Mapped Image Quality Index as the objective to be maximized in a much lower dimensional solution space. We show that the evolutionary approach results in significantly reduced computational effort.

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Notes

  1. 1.

    See Hansen et al. [11] for an overview of evolution strategy related terminology.

References

  1. Arnold, D.V.: Resampling versus repair in evolution strategies applied to a constrained linear problem. Evol. Comput. 21(3), 389–411 (2013)

    Article  Google Scholar 

  2. Banterle, F., Artusi, A., Debattista, K., Chalmers, A.: Advanced High Dynamic Range Imaging: Theory and Practice. AK Peters/CRC Press, Natick (2011)

    Book  Google Scholar 

  3. Čadík, M., Wimmer, M., Neumann, L., Artusi, A.: Image attributes and quality for evaluation of tone mapping operators. In: Proceedings of the 14th Pacific Conference on Computer Graphics and Applications, pp. 34–44 (2006)

    Google Scholar 

  4. Chisholm, S.B., Arnold, D.V., Brooks, S.: Tone mapping by interactive evolution. In: Proceedings of the 11th Annual Conference on Genetic and Evolutionary Computation, pp. 515–522 (2009)

    Google Scholar 

  5. Debevec, P.E., Malik, J.: Recovering high dynamic range radiance maps from photographs. In: Proceedings of the 24th Annual Conference on Computer Graphics and Interactive Techniques, pp. 369–378 (1997)

    Google Scholar 

  6. Durand, F., Dorsey, J.: Fast bilateral filtering for the display of high-dynamic-range images. ACM Trans. Graph. 21(3), 257–266 (2002)

    Article  Google Scholar 

  7. Fattal, R., Lischinski, D., Werman, M.: Gradient domain high dynamic range compression. ACM Trans. Graph. 21(3), 249–256 (2002)

    Article  Google Scholar 

  8. Gao, X., Brooks, S., Arnold, D.V.: Automated parameter tuning for tone mapping using visual saliency. Comput. Graph. 52, 171–180 (2015)

    Article  Google Scholar 

  9. Gao, X., Brooks, S., Arnold, D.V.: Automatic blended tone mapping through evolutionary optimization. In: Proceedings of the IEEE World Congress on Computational Intelligence, pp. 3855–3862 (2016)

    Google Scholar 

  10. Gu, K., Zhai, G., Liu, M., Yang, X., Zhang, W.: Details preservation inspired blind quality metric of tone mapping methods. In: Proceedings of the IEEE International Symposium on Circuits and Systems, pp. 518–521 (2014)

    Google Scholar 

  11. Hansen, N., Arnold, D.V., Auger, A.: Evolution strategies. In: Kacprzyk, J., Pedrycz, W. (eds.) Handbook of Computational Intelligence, pp. 871–898. Springer, Heidelberg (2015)

    Google Scholar 

  12. Ma, K., Yeganeh, H., Zeng, K., Wang, Z.: High dynamic range image compression by optimizing tone mapped image quality index. IEEE Trans. Image Process. 24(10), 3086–3097 (2015)

    Article  MathSciNet  Google Scholar 

  13. Mantiuk, R., Seidel, H.-P.: Modeling a generic tone-mapping operator. Comput. Graph. Forum 27(2), 699–708 (2008)

    Article  Google Scholar 

  14. Mantiuk, R., Daly, S., Kerofsky, L.: Display adaptive tone mapping. ACM Trans. Graph. 27(3), 68:1–68:10 (2008)

    Article  Google Scholar 

  15. Nafchi, H.Z., Shahkolael, A., Moghaddam, R.F., Mohamed, C.: FSITM: a feature similarity index for tone-mapped images. IEEE Sig. Process. Lett. 22(8), 1026–1029 (2015)

    Article  Google Scholar 

  16. Reinhard, E., Ward, G., Pattanaik, S., Debevec, P.: High Dynamic Range Imaging: Acquisition, Display, and Image-Based Lighting. Morgan Kaufmann Publishers, San Francisco (2005)

    Google Scholar 

  17. Tumblin, J., Turk, G.: LCIS: a boundary hierarchy for detail-preserving contrast reduction. In: Proceedings of the 26th Annual Conference on Computer Graphics and Interactive Techniques, pp. 83–90 (1999)

    Google Scholar 

  18. Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004)

    Article  Google Scholar 

  19. Yeganeh, H., Wang, Z.: Objective quality assessment of tone-mapped images. IEEE Trans. Image Process. 22(2), 657–667 (2013)

    Article  MathSciNet  MATH  Google Scholar 

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Acknowledgement

This research was supported by the Natural Sciences and Engineering Research Council of Canada (NSERC). The radiance maps used to evaluate the algorithms are due to J. Tumblin [17], G. Ward [16], D. Lischinski [7], M. Čadík [3], P. Debevec [5], and MathWorks.

Author information

Authors and Affiliations

  1. Faculty of Computer Science, Dalhousie University, Halifax, Nova Scotia, B3H 4R2, Canada

    Xihe Gao, Jeremy Porter, Stephen Brooks & Dirk V. Arnold

Authors
  1. Xihe Gao
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  2. Jeremy Porter
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  3. Stephen Brooks
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  4. Dirk V. Arnold
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Corresponding authors

Correspondence to Stephen Brooks or Dirk V. Arnold .

Editor information

Editors and Affiliations

  1. Inria, Thiverval-Grignon, France

    Evelyne Lutton

  2. Inria Bordeaux, University of Bordeaux, Talence, France

    Pierrick Legrand

  3. ECAM Strasbourg-Europe, Schiltigheim, France

    Pierre Parrend

  4. University of Tours, Tours, France

    Nicolas Monmarché

  5. Université Paris-Sud, Orsay, France

    Marc Schoenauer

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Gao, X., Porter, J., Brooks, S., Arnold, D.V. (2018). Evolutionary Optimization of Tone Mapped Image Quality Index. In: Lutton, E., Legrand, P., Parrend, P., Monmarché, N., Schoenauer, M. (eds) Artificial Evolution. EA 2017. Lecture Notes in Computer Science(), vol 10764. Springer, Cham. https://doi.org/10.1007/978-3-319-78133-4_13

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  • DOI: https://doi.org/10.1007/978-3-319-78133-4_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-78132-7

  • Online ISBN: 978-3-319-78133-4

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