This paper aims at presenting the complexity of the process of image target-based color correction (CC). We present issues encountered from acquisition to rendering using colorimetric traditional tools. Target-based CC can be seen as an optimization problem. We have tested SAT and HUE adaptive fine tuning (SHAFT) an automated framework for target-based CC. A key element of SHAFT is an iterative CIEDE2000 variation comparison between a reference and target image. In this work we replace the standard CIEDE2000 with the Euclidean color-difference formula for small–medium color differences in log-compressed Optical Society of America’s Committee on Uniform Color Scales (OSA-UCS) space. Results are presented using both formulae. A discussion on the complexity of scene color departures and correction performances concludes the paper. The effect of real scene complexity is shown and how colors are subject to disordered shifts in the color space. Because of this complexity, the role of the CC method as a different color error minimizer emerges.

中文翻译：

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图像色彩校正的复杂过程：对基于目标的框架的测试

本文旨在介绍基于图像目标的色彩校正（CC）过程的复杂性。我们介绍了使用比色传统工具从采集到渲染所遇到的问题。基于目标的CC可以看作是一个优化问题。我们已经测试了SAT和HUE自适应微调（SHAFT），这是一种基于目标CC的自动化框架。SHAFT的关键要素是参考图像和目标图像之间的迭代CIEDE2000变化比较。在这项工作中，我们用对数压缩的美国光学学会统一色标委员会（OSA-UCS）空间中的中小色差的欧几里德色差公式替换了标准CIEDE2000。结果用两个公式表示。对场景色彩偏移和校正性能的复杂性进行了讨论，得出了本文的结论。显示了真实场景复杂性的影响以及颜色如何受到颜色空间中无序移位的影响。由于这种复杂性，CC方法作为另一种颜色误差最小化器的作用应运而生。