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Providing a Single Ground-Truth for Illuminant Estimation for the ColorChecker Dataset.

Ghalia Hemrit, Graham D Finlayson, Arjan Gijsenij

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    Summary
    This summary is machine-generated.

    The widely used ColorChecker dataset has multiple ground-truth sets for illuminant estimation, leading to inconsistent algorithm rankings. This study proposes a unified ground-truth, crucial for accurate algorithm evaluation.

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    Area of Science:

    • Computer Vision
    • Image Processing
    • Color Science

    Background:

    • The ColorChecker dataset is a standard benchmark for evaluating illuminant estimation algorithms.
    • Existing ground-truth data for the ColorChecker dataset is inconsistent, with at least three different sets available.
    • This inconsistency can lead to conflicting conclusions about algorithm performance in scientific literature.

    Purpose of the Study:

    • To investigate the reasons behind the discrepancies in the existing ground-truth data for the ColorChecker dataset.
    • To introduce a new, unified, and recommended set of ground-truth data for the ColorChecker dataset.
    • To demonstrate the impact of different ground-truth datasets on the performance evaluation of illuminant estimation algorithms.

    Main Methods:

    • Analysis of the sources of variation among the three existing ground-truth datasets.
    • Development and validation of a new, single, and recommended ground-truth dataset.
    • Comparative experiments evaluating illuminant estimation algorithms using both legacy and the new ground-truth data.

    Main Results:

    • The study identifies the causes for the differences in the existing ground-truth datasets.
    • A new, recommended ground-truth dataset is proposed.
    • Experiments show that the ranking of illuminant estimation algorithms can be reversed based on the ground-truth data used.

    Conclusions:

    • The choice of ground-truth data significantly impacts the evaluation and ranking of illuminant estimation algorithms.
    • A standardized and validated ground-truth dataset is essential for reliable benchmarking.
    • The proposed unified ground-truth dataset provides a more consistent and accurate basis for algorithm development and comparison.