Complex process of image color correction: a test of a target-based framework.

Area of Science:

• Color Science
• Image Processing
• Computational Imaging

Background:

• Image target-based color correction (CC) is complex, involving challenges from acquisition to rendering.
• Traditional colorimetric tools often struggle with the nuances of real-world scene color variations.
• CC is fundamentally an optimization problem requiring robust methodologies.

Purpose of the Study:

• To analyze the complexity of image target-based color correction (CC).
• To evaluate the performance of the SAT and HUE adaptive fine tuning (SHAFT) framework for automated CC.
• To investigate the impact of replacing CIEDE2000 with the Euclidean color-difference formula in OSA-UCS space for CC.

Main Methods:

• Tested the automated SHAFT framework for target-based CC.
• Employed an iterative comparison of CIEDE2000 variations between reference and target images.
• Replaced standard CIEDE2000 with the Euclidean color-difference formula in log-compressed OSA-UCS space for evaluating small-medium color differences.

Main Results:

• Presented results comparing the standard CIEDE2000 formula with the Euclidean formula in OSA-UCS space.
• Demonstrated the effect of real scene complexity on color departures and CC performance.
• Highlighted disordered color shifts in the color space due to scene complexity.

Conclusions:

• The complexity of real scenes leads to significant color departures and disordered shifts.
• The SHAFT framework, particularly with the Euclidean formula in OSA-UCS space, shows potential for minimizing color errors.
• The choice of color difference formula impacts the effectiveness of CC methods in complex scenarios.