Optimum Parameter Estimation of Tone Mapping Operators by Natural Image Statistics

Daiki Okazaki, Kenji Hara, Kohei Inoue, Kiichi Urahama

Abstract


In this paper, we propose a method that optimizes the parameters of tone mapping operators by compressing the dynamic range of HDR images using natural image statistics.First, a prior probability model of a natural image is constructed for color natural images based on a generalized Gaussian distribution.Then, an LDR image is generated by converting the HDR image using the tone mapping operator. Next, we generate a normalized histogram of the LDR image using a discrete wavelet transformation.Finally, the optimal parameters of the tone mapping operator are estimated by minimizing the Kullbuck--Leibler divergence of the probability density function and the normalized histogram.Using these parameters, it is possible to generate an LDR image that closely resembles the natural image.


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References


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DOI: https://doi.org/10.12792/jiiae.7.106

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Online edition: ISSN 2187-8811 Print edition: ISSN 2188-1758