IEEE Trans Vis Comput Graph. 2019 Sep;25(9):2777-2790. doi: 10.1109/TVCG.2018.2855742. Epub 2018 Jul 19.
Pseudocoloring is one of the most common techniques used in scientific visualization. To apply pseudocoloring to a scalar field, the field value at each point is represented using one of a sequence of colors (called a colormap). One of the principles applied in generating colormaps is uniformity and previously the main method for determining uniformity has been the application of uniform color spaces. In this paper we present a new method for evaluating the feature detection threshold function across a colormap. The method is used in crowdsourced studies for the direct evaluation of nine colormaps for three feature sizes. The results are used to test the hypothesis that a uniform color space (CIELAB) will accurately model colormapped feature detection thresholds compared to a model where the chromaticity components have reduced weights. The hypothesis that feature detection can be predicted solely on the basis of luminance is also tested. The results reject both hypotheses and we demonstrate how reduced weights on the green-red and blue-yellow terms of the CIELAB color space creates a more accurate model when the task is the detection of smaller features in colormapped data. Both the method itself and modified CIELAB can be used in colormap design and evaluation.
伪彩色是科学可视化中最常用的技术之一。要将伪彩色应用于标量场，需要使用颜色序列中的一种颜色（称为色图）来表示每个点的场值。在生成色图时应用的原则之一是均匀性，以前确定均匀性的主要方法是应用均匀颜色空间。在本文中，我们提出了一种新的方法来评估色图上的特征检测阈值函数。该方法用于众包研究，直接评估了三个特征大小的九个色图。结果用于检验以下假设：与色图特征检测阈值相比，均匀颜色空间（CIELAB）将准确地对模型进行建模，而在模型中，色度分量的权重降低。还测试了特征检测仅基于亮度就可以进行预测的假设。结果否定了这两个假设，并演示了当任务是检测色图数据中的较小特征时，CIELAB 颜色空间的绿色-红色和蓝色-黄色项的权重降低如何创建更准确的模型。该方法本身和修改后的 CIELAB 都可以用于色图设计和评估。
