EuroRVVV: EuroVis Workshop on Reproducibility, Verification, and Validation in Visualization

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Browsing EuroRVVV: EuroVis Workshop on Reproducibility, Verification, and Validation in Visualization by Subject "Colormapping"

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    A Crowdsourced Approach to Colormap Assessment
    (The Eurographics Association, 2017) Turton, Terece L.; Ware, Colin; Samsel, Francesca; Rogers, David H.; Kai Lawonn and Noeska Smit and Douglas Cunningham
    Despite continual research and discussion on the perceptual effects of color in scientific visualization, psychophysical testing is often limited. In-person lab studies can be expensive and time-consuming while results can be difficult to extrapolate from meticulously controlled laboratory conditions to the real world of the visualization user. We draw on lessons learned from the use of crowdsourced participant pools in the behavioral sciences and information visualization to apply a crowdsourced approach to a classic psychophysical experiment assessing the ability of a colormap to impart metric information. We use an online presentation analogous to the color key task from Ware's 1988 paper, Color Sequences for Univariate Maps, testing colormaps similar to those in the original paper along with contemporary colormap standards and new alternatives in the scientific visualization domain. We explore the issue of potential contamination from color deficient participants and establish that perceptual color research can appropriately leverage a crowdsourced participant pool without significant CVD concerns. The updated version of the Ware color key task also provides a method to assess and compare colormaps.
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    Evaluating the Perceptual Uniformity of Color Sequences for Feature Discrimination
    (The Eurographics Association, 2017) Ware, Colin; Turton, Terece L.; Samsel, Francesca; Bujack, Roxana; Rogers, David H.; Kai Lawonn and Noeska Smit and Douglas Cunningham
    Probably the most common method for visualizing univariate data maps is through pseudocoloring and one of the most commonly cited requirements of a good colormap is that it be perceptually uniform. This means that differences between adjacent colors in the sequence be equally distinct. The practical value of uniformity is for features in the data to be equally distinctive no matter where they lie in the colormap, but there are reasons for thinking that uniformity in terms of feature detection may not be achieved by current methods which are based on the use of uniform color spaces. In this paper we provide a new method for directly evaluating colormaps in terms of their capacity for feature resolution. We apply the method in a study using Amazon Mechanical Turk to evaluate seven colormaps. Among other findings the results show that two new double ended sequences have the highest discriminative power and good uniformity. Ways in which the technique can be applied include the design of colormaps for uniformity, and a method for evaluating colormaps through feature discrimination curves for differently sized features.