A poorly chosen colormap between values and colors can fool the human eyes to, e.g., pick out non-existing features, or to hide important features.
- Perceptual Uniformity
- Printing in B/W
- 3d surfaces and light shading
Some colormaps (rainbow colormaps in particular) do not scale well for people with CVD (color blindness).
the lightness of the color in an image is a fair representation of its scalar values
non-uniformality in lightness is the main reason of faulty feature extractions from image, (banding)
isoluminant: Having uniform light intensity
Printing in B/W
Some colormaps, do not provide the same level of information when converted to black/white.
3d surfaces and light shading
in a 3D scene, shading cues, which are themselves changes in brightness, are vital to understanding shapes. Thus, you have to avoid having the brightness changes in the color map interfere with the brightness changes in shading and vice versa. You achieve this by limiting the color map to reasonably bright colors. Because this reduces the total range of brightness in the color map, I find it most effective to use a diverging (double-ended) color map.
Type of Colormaps
Rainbow maps (and why they suck)
Perceptually Uniform Sequential colormaps
This is great python notebook from the team at EHT (Event Horizon Telescope), scroll down for the section where they talk about uniformiziing non-uniform colormaps. https://github.com/liamedeiros/ehtplot/blob/docs/docs/COLORMAPS.ipynb
‘viridis’, ‘plasma’, ‘inferno’, ‘magma’, ‘cividis’
a series of color maps that are designed to improve graph readability for readers with common forms of color blindness and/or color vision deficiency. The color maps are also perceptually-uniform, both in regular form and also when converted to black-and-white for printing.
Based off of viridis, but optimized for all viewers, specifically those with CVD.
This are colormaps/gradients used to show polarization.
https://imagej.net/plugins/glasbey - Glasbey LUT (lookup table) - designed to be “maximally discontinuous”. Colors next to each other are very differnt from on another.
LUT (Color Lookup Tables)
- https://gist.github.com/endolith/2719900#id7 - Documenting matlab color plots -0 contains some great descriptions, context and history about differnet color maps.
- https://medium.com/techtalkers/photos-of-space-are-actually-black-and-white-heres-how-they-re-colored-d43561641ac3 - Colorizing astronomy photography
- https://observablehq.com/@d3/color-schemes - D3’s colormaps page
- https://carto.com/carto-colors/ - Cartographic colormaps, you know, for using in maps.
- https://colorbrewer2.org/# - Excellent tool for testing color maps on a … map.
- EHT use of color to create a uniform colormap for viewing blackholes
- Scientific colour Maps - www.fabiocrameri.ch
- Introduction to the viridis color maps - cran.r-project.org
- Diverging Color Maps for Scientific Visualization - kennethmoreland.com
- Color Map Advice for Scientific Visualization - kennethmoreland.com
- Moreland, K. (2009). Diverging Color Maps for Scientific Visualization. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2009. Lecture Notes in Computer Science, vol 5876. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10520-3_9
- R. Bujack, T. L. Turton, F. Samsel, C. Ware, D. H. Rogers and J. Ahrens, “The Good, the Bad, and the Ugly: A Theoretical Framework for the Assessment of Continuous Colormaps,” in IEEE Transactions on Visualization and Computer Graphics, vol. 24, no. 1, pp. 923-933, Jan. 2018, doi: 10.1109/TVCG.2017.2743978.
- J. R. Nuñez, C. R. Anderton, and R. S. Renslow, “Optimizing colormaps with consideration for color vision deficiency to enable accurate interpretation of scientific data,” PLOS ONE, 2018. 13(7): p. e0199239 - Cividis colormap paper
- https://www.vis4.net/palettes -Generate different color palettes