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    This study introduces a new colorimetric correction method using a wide gamut spectral dataset to reduce errors in digital color imaging. The enhanced method significantly improves accuracy, especially for high-saturated colors.

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    Area of Science:

    • Digital Color Imaging
    • Color Science
    • Image Processing

    Background:

    • Cross-media color transformations suffer from significant errors due to variations in lighting and devices.
    • Existing chromatic adaptation and regression-based methods have limitations, particularly with high-saturated colors, due to small training sample gamuts.

    Purpose of the Study:

    • To reduce colorimetric correction errors in high-saturated regions within digital color imaging workflows.
    • To enhance the accuracy of color transformations across different digital media.

    Main Methods:

    • Developed a wide gamut spectral dataset by collecting and producing diverse spectral color samples.
    • Implemented gamut partitioning and optimal color purity selection to create optimal training samples.
    • Modified existing regression-based colorimetric correction models using the enhanced dataset.

    Main Results:

    • The modified colorimetric correction method demonstrated a significant improvement in performance over existing techniques.
    • Overall colorimetric correction errors were reduced by approximately 15% (CIEDE2000 color difference).
    • Error reduction in high-saturated color regions reached approximately 40%.

    Conclusions:

    • The proposed modified colorimetric correction method effectively reduces errors, particularly in challenging high-saturated color areas.
    • The use of a wide gamut spectral dataset provides optimal training data for improved colorimetric correction.
    • This research offers valuable support for advancing digital color imaging technologies.