Nagoor, Omniah H.Borgo, RitaJones, Mark W.Tao Ruan Wan and Franck Vidal2017-09-212017-09-212017978-3-03868-050-5https://doi.org/10.2312/cgvc.20171280https://diglib.eg.org:443/handle/10.2312/cgvc20171280The choice of a mapping from data to color should involve careful consideration in order to maximize the user understanding of the underlying data. It is desirable for features within the data to be visually separable and identifiable. Current practice involves selecting a mapping from predefined colormaps or coding specific colormaps using software such as MATLAB. The purposes of this paper are to introduce interactive operations for colormaps that enable users to create more visually distinguishable pixel based visualizations, and to describe our tool, Data Painter, that provides a fast, easy to use framework for defining these color mappings. We demonstrate the use of the tool to create colormaps for various application areas and compare to existing color mapping methods. We present a new objective measure to evaluate their efficacyHumancentered computingScientific visualizationVisual analyticsVisualization toolkits10.2312/cgvc.2017128069-76