Measuring and Modeling the Feature Detection Threshold Functions of Colormaps

Abstract

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. Here we present a new method for evaluating the feature discrimination 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) gives too much weight to chromatic differences compared to luminance differences because of the way it was constructed. The hypothesis that feature discrimination 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.

Well-Posed Geoscientific Visualization Through Interactive Color Mapping

“…Our visualizations demonstrate that color-mapping should be conducted with great care and that extant mappings can be improved. Building on Kovesi (2015), Ware et al (2018) and Crameri (2018a), we employed CIELAB color space to visualize continuous geoscience data.…”

“…The widelyused rainbow ("Jet") or spectrum-approximation colormap, whilst having specific productive use-cases, is well-known for introducing problems of perceptual non-linearity, hueordering ambiguity and loss of visual discrimination for fine detail (Rogowitz and Treish, 1998;Eddins, 2014;Hawkins, 2015;Stauffer et al, 2015). Research into optimal colormap design for science has an extensive literature (Silva et al, 2011;Kovesi, 2015;Moreland, 2016;Ware et al, 2018), including optimization for color vision deficiencies (Light and Bartlein, 2004). Addressing the need for consistent terminology, Bujack et al (2018) propose a nomenclature with unambiguous mathematical definitions, characteristics that are quantifiable via their on-line tool (Bujack et al, 2018).…”

“…Colormaps that maximize color difference such as Viridis, Magma, Parula and others are now becoming default schemes in widely-used scientific software (Smith et al, 2015). Ware et al (2018) and Kovesi (2015) provide thorough analysis of a range of colormaps for different visualization tasks. Crameri (2018a,b) provides extensive discussion of colormaps for geoscientific visualization, as well as software and colormap resources for widely used programs such as GMT, Matlab, QGIS and others.…”

“…This figure shows a schematic model of the visualization process, incorporating human-computer interaction. Moreland, 2016;Ware et al, 2018). Most importantly for data visualization, there is no meaningful metric for representing color difference in RGB, HSL, or HSV color spaces that match human perception of color differences (Robertson, 1988).…”

“…CIELAB and other UCS are commonly proposed for creating perceptually uniform color sequences in data visualization (Meyer and Greenberg, 1980;Kovesi, 2015;Ware et al, 2018), despite known limitations and deficiencies (Wyszecki and Stiles, 1982;Sharma and Rodriguez-Pardo, 2012;Fairchild, 2013;Zeyen et al, 2018). They form a set of absolute color spaces defined against reference whites or standard illuminants defined by the CIE (Tkalcic and Tasic, 2003;Foster, 2008;Fairchild, 2013).…”