The Good, the Bad, and the Ugly: A Theoretical Framework for the Assessment of Continuous Colormaps

Bujack, Roxana; Turton, Terece L.; Samsel, Francesca; Ware, Colin; Rogers, David; Ahrens, James

doi:10.1109/tvcg.2017.2743978

Cited by 69 publications

(79 citation statements)

References 62 publications

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“…Approaches to color ramp design combine aspects of color perception and aesthetics. Guidelines about what makes color ramps effective are often derived from designer experience or empirical data (see Bujack et al [12] and Zhou & Hansen [92] for surveys). These guidelines can be either perceptual (e.g., colors should be discriminable) or aesthetic (e.g., colors should be harmonious).…”

Section: Guidelines and Techniquesmentioning

confidence: 99%

“…For example, Sloan & Brown [70] recommends that color ramps should use "a set of colors with an easily remembered order." While recent efforts mathematically formalize these heuristics into constraints (e.g., Bujack et al [12]), these formalizations still require substantial manual guiding to get from abstract constraints to concrete encodings. Novice visualization designers are currently left with two choices for generating color ramps: to rely on their limited intuitions to craft color ramps of unverified quality or to choose from a small, predefined set of high-quality ramps in tools such as ColorBrewer [27].…”

Section: Introductionmentioning

confidence: 99%

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Color Crafting: Automating the Construction of Designer Quality Color Ramps

Smart

Szafir

2020

IEEE Trans. Visual. Comput. Graphics

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Fig. 1. Our approach uses design mining and unsupervised clustering techniques to produce automatically generated color ramps that capture designer practices. The four choropleth maps shown above utilize color ramps generated from our approach. Developers select a single guiding seed color, shown in the squares below each map, to generate a ramp. We then fit curves capturing structural patterns in designer practices in CIELAB (bottom) to these seed colors to generate ramps (middle, seed colors indicated by a black dot). We embody this technique in Color Crafter, a web-based tool that enables designers of all ability levels to generate high-quality custom color ramps.Abstract-Visualizations often encode numeric data using sequential and diverging color ramps. Effective ramps use colors that are sufficiently discriminable, align well with the data, and are aesthetically pleasing. Designers rely on years of experience to create high-quality color ramps. However, it is challenging for novice visualization developers that lack this experience to craft effective ramps as most guidelines for constructing ramps are loosely defined qualitative heuristics that are often difficult to apply. Our goal is to enable visualization developers to readily create effective color encodings using a single seed color. We do this using an algorithmic approach that models designer practices by analyzing patterns in the structure of designer-crafted color ramps. We construct these models from a corpus of 222 expert-designed color ramps, and use the results to automatically generate ramps that mimic designer practices. We evaluate our approach through an empirical study comparing the outputs of our approach with designer-crafted color ramps. Our models produce ramps that support accurate and aesthetically pleasing visualizations at least as well as designer ramps and that outperform conventional mathematical approaches.

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Section: Guidelines and Techniquesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Color Crafting: Automating the Construction of Designer Quality Color Ramps

Smart

Szafir

2020

IEEE Trans. Visual. Comput. Graphics

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“…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).…”

Section: Colormaps and Color Scales In Scientific Visualizationmentioning

confidence: 99%

“…Plotting CIE XYZ tristimulus values in Cartesian coordinates produce perceptually non-uniform color spaces (CIE, 2007). Uniform Color Spaces (UCS) are mathematical transformations of the CIE 1931 XYZ gamut that represent color in a perceptually even fashion, defined by the CIE as a color space in which equal metric distances approximately predict and represent equal perceived color differences (Luo et al, 2006;Bujack et al, 2018). As perception-based color models, they more accurately map the human visual gamut and mitigate color-matching and colordifference problems.…”

Section: Uniform Color Space: Cielabmentioning

confidence: 99%

Well-Posed Geoscientific Visualization Through Interactive Color Mapping

2019

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Scientific visualization aims to present numerical values, or categorical information, in a way that enables the researcher to make an inference that furthers knowledge. Well-posed visualizations need to consider the characteristics of the data, the display environment, and human visual capacity. In the geosciences, visualizations are commonly applied to spatially varying continuous information or results. In this contribution we make use of a suite of newly written computer applications which enable spatially varying data to be displayed in a performant graphics environment. We present a comparison of color-mapping using illustrative color spaces (RGB, CIELAB). The interactive applications display the gradient paths through the chosen color spaces. This facilitates the creation of color-maps that accommodate the non-uniformity of human color perception, producing an image where genuine features are seen. We also take account of aspects of a dataset such as parameter uncertainty. For an illustrative case study using a seismic tomography result, we find that the use of RGB color-mapping can introduce non-linearities in the visualization, potentially leading to incorrect inference. Interpolation in CIELAB color space enables the creation of perceptually uniform linear gradients that match the underlying data, along with a simply computable metric for color difference, E. This color space assists accuracy and reproducibility of visualization results. Well-posed scientific visualization requires both "visual literacy" and "visual numeracy" on an equal footing with clearly written text. It is anticipated that this current work, with the included color-maps and software, will lead to wider usage of informed color-mapping in the geosciences.

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“…There are three important properties of colormaps: discriminative power, uniformity, and order [23]. Discriminative power depicts the number of specific colors that viewers can identify on a color scale.…”

Section: Colormaps and Evaluation Studiesmentioning

confidence: 99%

A Study of Colormaps in Network Visualization

et al. 2019

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Color is a widely used visual channel for encoding data in visualization design. It is important to select the appropriate type of color mapping to better understand the data. While several studies have investigated the effects of colormaps in various types of information visualization, there have been no studies on their effects on network visualization. Thus, in this paper, we investigate the effects of several colormaps in network visualization using node-link diagrams. Specifically, we compare four different single- and multi-hue colormaps for node attributes, and evaluate their effectiveness in terms of task completion time and correctness rate. Our results show that participants complete their tasks significantly faster with blue (single-hue, sequential) as compared to viridis (multi-hue, sequential), RdYlBu (divergent, red-yellow-blue), and jet (rainbow) colormaps. Additionally, the overall correctness rate shows significant differences between colormaps, with viridis being the least error-prone among the colormaps studied.

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The Good, the Bad, and the Ugly: A Theoretical Framework for the Assessment of Continuous Colormaps

Cited by 69 publications

References 62 publications

Color Crafting: Automating the Construction of Designer Quality Color Ramps

Color Crafting: Automating the Construction of Designer Quality Color Ramps

Well-Posed Geoscientific Visualization Through Interactive Color Mapping

A Study of Colormaps in Network Visualization

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