物体表面色的白度模型研究

物体表面色的白度对于广泛存在的白色物体而言是一个极为重要的颜色属性，而白度会随着照明和视觉情况的改变而变化。尤其是对于含有荧光增白剂的物体而言，光源的光谱将直接影响荧光增白剂的激发效果及白度。相比于众多针对物体表面色各种颜色属性的研究，针对白度的研究极为缺乏，至今尚未在色貌模型及均匀色空间中有针对物体表面色白度的相关研究。目前使用最为广泛的CIE白度值仅仅基于物体表面色在D65标准照明体这一单一照明情况下的(x,y)色坐标，该白度值不会随照明(色温、照度、光谱)和视觉(适应场亮度)情况的变化而变化，因而无法准确评价及预测物体表面色的实际白度。本项目使用已搭建的14通道光谱可调照明平台辅以遗传算法高质量地模拟不同色温的黑体辐射及自然光光谱，通过开展心理物理实验来研究在各种照明及视觉情况下广泛均匀分布于颜色空间内的各种颜色样本的白度，从而建立一个可以准确评价及预测物体表面色在实际情况下的白度。

The whiteness of a surface color is an important color attributes for the white objects that are widely appear around us. The appearance of these white objects will change under different illumination and viewing conditions, especially for those which contain fluorescent whitening agents (FWAs) due to the impact of the spectral power distribution of the illumination on the excitation of the FWAs. In comparison to the numerous investigations on various color attributes for surface colors, the whiteness appearance received little attention. No research has been done to investigate the whiteness appearance of surface colors using color appearance models or in uniform color spaces. The CIE whiteness index, the most widely used metric, characterizes the whiteness appearance of a surface color only using its chromaticities under CIE D65 illuminant and remains constant under different illumination and viewing conditions, which makes it fail to characterize the actual whiteness appearance. This project makes use of the customized 14-channel spectrally tuneable lighting platform to simulate the spectra of blackbody radiators and daylight illuminants with different CCTs through a genetic algorithm and investigates the whiteness appearance of various color samples that are widely and uniformed distributed in a uniform color space under different illumination and viewing conditions through psychophysical experiments. It aims to develop a whiteness model that can accurately characterize and predict the whiteness appearance of surface colors.