概括
染色适应变换 (CAT) 模型无法准确预测环境照明适应. 修订后的CAT采用了照明色度和发光度,可以改善数字成像自动白平衡 (AWB) 的预测.
科学领域:
- 颜色科学 颜色科学
- 视觉感知 视觉感知 视觉感知
- 数字成像技术的数字成像.
背景情况:
- 染色适应转换 (CAT) 是一个全面的模型.
- 现有的CAT模型是基于对象颜色匹配,而不是照明适应.
- 照明颜色决定了场景的感知色调.
研究的目的:
- 评估各种照明器对环境照明的色彩适应性.
- 在不同的照明条件下调查现有的CAT模型的准确性.
- 开发一个修订的CAT模型,以改善环境照明适应预测.
主要方法:
- 进行了跨媒体颜色匹配实验.
- 观察者在黑暗中将显示白点与现实世界的场景色调相匹配.
- 测试了各种各样的照明剂,颜色和发光度各不相同.
主要成果:
- 现有的基于亮度的CAT模型不准确地预测了环境照明适应.
- 一个经过修订的CAT模型,包括照明色度和发光度,显示了更好的准确性.
- 现有模型在预测适应环境光线方面存在明显的局限性.
结论:
- 目前的CAT模型不足以预测适应环境照明的情况.
- 修订后的CAT模型为自动白平衡 (AWB) 提供了更好的感知一致性.
- 这些发现提升了对视觉适应的理解,并具有实际的成像应用.
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