Evolutionary Optimization of Tone Mapped Image Quality Index

  • Xihe Gao
  • Jeremy Porter
  • Stephen BrooksEmail author
  • Dirk V. ArnoldEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10764)


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.


High dynamic range imaging Tone mapping Optimization Evolution strategy Image quality assessment 



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.


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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Faculty of Computer ScienceDalhousie UniversityHalifaxCanada

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