在光学条件下的亮度感知:实验和建模与S-cone和ipRGC的贡献
Tran Quoc Khanh1, Peter Bodrogi2, Babak Zandi1
1Laboratory of Adaptive Lighting Systems and Visual Processing, Department of Electrical Engineering and Information Technology, Technical University of Darmstadt, 64289, Darmstadt, Germany.
Scientific reports
|September 4, 2023
概括
人类的亮度感知不仅仅涉及物理光的测量. 这项研究表明,S圆和内在光敏感的视网膜质细胞 (ipRGCs) 显著促进感知亮度,改善视觉模型.
科学领域:
- 摄影测量和视觉科学科学 摄影测量和视觉科学
- 人类的视觉感知 人类的视觉感知
- 测色仪的颜色测量方法
背景情况:
- 1924年的CIE光学光效率函数标准化了物理亮度测量 (流明,卢克斯,cd/m2).
- 人类的亮度感知是复杂的,涉及L圆,M圆,S圆,棒,以及内在光敏感的视网膜细胞 (ipRGCs).
- 之前的模型,比如Fotios等. ,部分考虑了这些因素,但缺乏全面的整合.
研究的目的:
- 研究各种信号组件在光学条件下对人类亮度感知的贡献.
- 开发一个优化的视觉亮度模型,结合多个光感应器和ipRGC信号.
- 将新型号的性能与现有的亮度模型进行比较.
主要方法:
- 对28名受试者进行视觉实验,让他们接触到25种不同的光谱.
- 收集了关于感知亮度的数据,并分析了L圆,M圆,S圆,棒和ipRGCs的贡献.
- 在实验数据库上利用了优化过程来构建新的亮度模型.
主要成果:
- 优化的模型表明,S圆和ipRGC在人类的亮度感知中起着重要的作用,与标准的光效率功能一起.
- 这款新车型实现了0.9554的高R2和4.7802.2的低RMSE.
- 结果显著超过了Fotios等人. 这两种模型是R2=0.8161,RMSE=9.7123和传统模型 (R2=0.8121,RMSE=9.8171).
结论:
- 人类的亮度感知是多个视觉系统组件的复杂相互作用,不仅仅由光学光效率决定.
- 开发的视觉缩放亮度模型,结合照度,S圆和ipRGCs,提供了更准确的人类感知表现.
- 这项研究为更复杂的照明设计和视觉感知研究提供了基础.
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