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Generic color correction for tone mapping operators in high dynamic range imaging.

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    This study introduces an automatic color correction model for tone mapping that preserves image lightness and hue. The new method enhances color reproduction in high dynamic range (HDR) images, improving perceptual quality.

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    Area of Science:

    • Image processing
    • Computer vision
    • Color science

    Background:

    • Traditional tone mapping operators (TMOs) often neglect color information, leading to suboptimal color reproduction in high dynamic range (HDR) images.
    • Existing color management techniques may require manual adjustments or introduce undesirable shifts in lightness and hue.

    Purpose of the Study:

    • To develop an automatic color correction model for tone-mapped images that aligns with human color perception.
    • To achieve optimal color reproduction in tone-mapped images without altering lightness or hue significantly.

    Main Methods:

    • The proposed model utilizes CIECAM16 perceptual correlates, including lightness, chroma, and hue.
    • The approach is adaptive and fully automatic, preserving the original lightness and hue of the HDR image.
    • No additional gamut mapping correction is required for natural color reproduction.

    Main Results:

    • Objective and subjective validation demonstrated the model's effectiveness.
    • The model successfully preserved the lightness of the tone-mapped image and the hue from the original HDR image.
    • Experimental results confirmed optimal color reproduction, particularly in terms of color naturalness.

    Conclusions:

    • The developed color correction model offers a significant improvement over traditional TMOs by prioritizing perceptual color quality.
    • This automatic and adaptive method provides natural color reproduction for tone-mapped HDR images.
    • The model's reliance on CIECAM16 ensures alignment with psychophysical color appearance phenomena.