A Survey of Colormaps in Visualization
Abstract
Colormaps are a vital method for users to gain insights into data in a visualization. With a good choice of colormaps, users are able to acquire information in the data more effectively and efficiently. In this survey, we attempt to provide readers with a comprehensive review of colormap generation techniques and provide readers a taxonomy which is helpful for finding appropriate techniques to use for their data and applications. Specifically, we first briefly introduce the basics of color spaces including color appearance models. In the core of our paper, we survey colormap generation techniques, including the latest advances in the field by grouping these techniques into four classes: procedural methods, user-study based methods, rule-based methods, and data-driven methods; we also include a section on methods that are beyond pure data comprehension purposes. We then classify colormapping techniques into a taxonomy for readers to quickly identify the appropriate techniques they might use. Furthermore, a representative set of visualization techniques that explicitly discuss the use of colormaps is reviewed and classified based on the nature of the data in these applications. Lastly, our paper is also intended to be a reference of colormap choices for readers when they are faced with similarmore »
- Authors:
-
- Univ. of Stuttgart (Germany). Visualisierungsinstitute
- Univ. of Utah, Salt Lake City, UT (United States). Scientific Computing and Imaging (SCI) Inst. and School of Computing
- Publication Date:
- Research Org.:
- Univ. of Utah, Salt Lake City, UT (United States)
- Sponsoring Org.:
- USDOE National Nuclear Security Administration (NNSA)
- OSTI Identifier:
- 1467452
- Grant/Contract Number:
- NA0002375; SC0007446
- Resource Type:
- Accepted Manuscript
- Journal Name:
- IEEE Transactions on Visualization and Computer Graphics
- Additional Journal Information:
- Journal Volume: 22; Journal Issue: 8; Journal ID: ISSN 1077-2626
- Publisher:
- IEEE
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 97 MATHEMATICS AND COMPUTING; Color; colormap; visualization; perception; survey
Citation Formats
Zhou, Liang, and Hansen, Charles D. A Survey of Colormaps in Visualization. United States: N. p., 2016.
Web. doi:10.1109/TVCG.2015.2489649.
Zhou, Liang, & Hansen, Charles D. A Survey of Colormaps in Visualization. United States. https://doi.org/10.1109/TVCG.2015.2489649
Zhou, Liang, and Hansen, Charles D. Mon .
"A Survey of Colormaps in Visualization". United States. https://doi.org/10.1109/TVCG.2015.2489649. https://www.osti.gov/servlets/purl/1467452.
@article{osti_1467452,
title = {A Survey of Colormaps in Visualization},
author = {Zhou, Liang and Hansen, Charles D.},
abstractNote = {Colormaps are a vital method for users to gain insights into data in a visualization. With a good choice of colormaps, users are able to acquire information in the data more effectively and efficiently. In this survey, we attempt to provide readers with a comprehensive review of colormap generation techniques and provide readers a taxonomy which is helpful for finding appropriate techniques to use for their data and applications. Specifically, we first briefly introduce the basics of color spaces including color appearance models. In the core of our paper, we survey colormap generation techniques, including the latest advances in the field by grouping these techniques into four classes: procedural methods, user-study based methods, rule-based methods, and data-driven methods; we also include a section on methods that are beyond pure data comprehension purposes. We then classify colormapping techniques into a taxonomy for readers to quickly identify the appropriate techniques they might use. Furthermore, a representative set of visualization techniques that explicitly discuss the use of colormaps is reviewed and classified based on the nature of the data in these applications. Lastly, our paper is also intended to be a reference of colormap choices for readers when they are faced with similar data and/or tasks.},
doi = {10.1109/TVCG.2015.2489649},
journal = {IEEE Transactions on Visualization and Computer Graphics},
number = 8,
volume = 22,
place = {United States},
year = {Mon Aug 01 00:00:00 EDT 2016},
month = {Mon Aug 01 00:00:00 EDT 2016}
}
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