A Retargeting Approach for Mesopic Vision: Simulation and Compensation
By Mehdi Rezagholizadeh, Tara Akhavan, Afsoon Soudi, Hannes Kaufmann, and James J. Clark
Abstract
Retargeting approaches aim at providing a uni ed framework for image rendering in which both the intended scene luminance and the actual luminance of the display are taken into account. At the core of any color retargeting method, a color vision model and its inverse are employed. Such a color appearance model should be invertible and cover the entire luminance range of the human visual system. There are not many available models which meet these two conditions. Moreover, most of these models are developed based on psychophysical experiments over color patches, and many have never been used for complex images due to their complexity. In this research, a color retargeting approach based on the mesopic model of Shin et al. [1] is developed to work with complex images. We propose an inverse model for complex images to compensate for color appearance changes on dimmed displays viewed in dark environment. Our experimental results using both quantitative and qualitative evaluations show a discriminative improvement in the perceived color quality for mesopic vision. The proposed method can be incorporated into image retargeting techniques and display rendering mechanisms.
Reference
M. Rezagholizadeh, T. Akhavan, A. Soudi, H. Kaufmann, J. Clark: "A Retargeting Approach for Mesopic Vision: Simulation and Compensation"; Journal of Imaging Science & Technology, 60 (2016), 1; 1 - 12.
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