色域
色调
色空间
显色指数
人工智能
计算机科学
色差
样品(材料)
集合(抽象数据类型)
计算机视觉
数学
RGB颜色空间
度量(数据仓库)
国际商会简介
稳健性(进化)
统计
颜色模型
光学
图像处理
彩色图像
数据挖掘
物理
白光
GSM演进的增强数据速率
化学
图像(数学)
基因
程序设计语言
热力学
生物化学
出处
期刊:Leukos
[Taylor & Francis]
日期:2018-10-17
卷期号:15 (1): 29-53
被引量:8
标识
DOI:10.1080/15502724.2018.1500485
摘要
This article examines how the color sample set, color space, and other calculation elements influence the quantification of gamut area. The IES TM-30-18 Gamut Index (Rg) serves as a baseline, with comparisons made to several other measures documented in scientific literature and 12 new measures formulated for this analysis using various components of existing measures. The results demonstrate that changes in the color sample set, color space, and calculation procedure can all lead to substantial differences in light source performance characterizations. It is impossible to determine the relative “accuracy” of any given measure outright, because gamut area is not directly correlated with any subjective quality of an illuminated environment. However, the utility of different approaches was considered based on the merits of individual components of the gamut area calculation and based on the ability of a measure to provide useful information within a complete system for evaluating color rendition. For gamut area measures, it is important to have a reasonably uniform distribution of color samples (or averaged coordinates) across hue angle—avoiding exclusive use of high-chroma samples—with sufficient quantity to ensure robustness but enough difference to avoid incidents of the hue-angle order of the samples varying between the test and reference conditions. It is also important to use a modern, uniform color space that is suitable for the quantification of color appearance and color difference.
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