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摘要翻译：
色觉缺陷(CVD)影响超过4%的人口，并导致不同的视觉感知的颜色。尽管这已经被人们知道了几十年，但在视觉光谱中有许多颜色的彩色地图经常被用来表示数据，导致有这种缺陷的人可能会误解或难以解释。在这里介绍的模块创建之前，没有使用现代颜色外观模型对CVD进行数学优化的colormaps。虽然有一些尝试为CVD患者制作美观或主观上可容忍的彩色地图，但我们的目标是制作优化的彩色地图，以便尽可能多的观众对科学数据进行最准确的感知。我们开发了一个Python模块cmaputil来创建CVD优化的colormaps，该模块导入colormaps，并将其修改为在CVD安全的colorspace中感知一致，同时线性化和最大化亮度范围。该模块提供给科学界，使其他人能够轻松地创建自己的CVDoptimized ColorMaps。在这里，我们给出了一个例子CVD优化的彩色地图创建与这个模块，是优化的查看那些没有CVD以及那些红绿色盲。这种colormap,cividis,能够对两个组进行几乎相同的视觉数据解释，在色调和亮度上是一致的，并且亮度线性增加。
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英文标题：
《Optimizing colormaps with consideration for color vision deficiency to
enable accurate interpretation of scientific data》
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作者：
Jamie R. Nu\~nez, Christopher R. Anderton, Ryan S. Renslow
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最新提交年份：
2018
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分类信息：
一级分类：Computer Science 计算机科学
二级分类：Computer Vision and Pattern Recognition 计算机视觉与模式识别
分类描述：Covers image processing, computer vision, pattern recognition, and scene understanding. Roughly includes material in ACM Subject Classes I.2.10, I.4, and I.5.
涵盖图像处理、计算机视觉、模式识别和场景理解。大致包括ACM课程I.2.10、I.4和I.5中的材料。
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一级分类：Quantitative Biology 数量生物学
二级分类：Other Quantitative Biology 其他定量生物学
分类描述：Work in quantitative biology that does not fit into the other q-bio classifications
不适合其他q-bio分类的定量生物学工作
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英文摘要：
Color vision deficiency (CVD) affects more than 4% of the population and leads to a different visual perception of colors. Though this has been known for decades, colormaps with many colors across the visual spectra are often used to represent data, leading to the potential for misinterpretation or difficulty with interpretation by someone with this deficiency. Until the creation of the module presented here, there were no colormaps mathematically optimized for CVD using modern color appearance models. While there have been some attempts to make aesthetically pleasing or subjectively tolerable colormaps for those with CVD, our goal was to make optimized colormaps for the most accurate perception of scientific data by as many viewers as possible. We developed a Python module, cmaputil, to create CVD-optimized colormaps, which imports colormaps and modifies them to be perceptually uniform in CVD-safe colorspace while linearizing and maximizing the brightness range. The module is made available to the science community to enable others to easily create their own CVDoptimized colormaps. Here, we present an example CVD-optimized colormap created with this module that is optimized for viewing by those without a CVD as well as those with redgreen colorblindness. This colormap, cividis, enables nearly-identical visual-data interpretation to both groups, is perceptually uniform in hue and brightness, and increases in brightness linearly.
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PDF链接：
https://arxiv.org/pdf/1712.01662
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