Perceptually Uniform
Perceptually uniform colour palettes are designed such that the change in colour is perceptually uniform throughout the palette. These palettes are grouped into two types: Large Brightness Range (not recommended for 3D surfaces) and Small Brightness Range (best for 3D surfaces).
Perceptually uniform means that the visible difference between any two colours in a palette only depends on how far apart they are on the palette. The actual hue, saturation, or brightness of each colour is irrelevant.
In other words, the difference between two colours at the dark end of a palette, separated by 10 units (in the colour space), will appear to be the same as two colours separated by 10 units at the bright end of the palette.
The advantages of this uniformity are to:
- Avoid flat spots, where locally low perceptual contrast can hide small but important details
- Avoid false anomalies, where locally high contrast can appear to represent a structure that isn't really there.
(Magma, Inferno, Plasma and Viridis) Stéfan van der Walt, Nathaniel Smith and Eric Firing. https://bids.github.io/colormap/
(Mako and Rocket) https://seaborn.pydata.org/tutorial/color_palettes.html
(Cividis) Optimizing colormaps with consideration for color vision deficiency to enable accurate interpretation of scientific data. Nuñez, J.R., Anderton, C.R., Renslow, R.S., 2018. PLOS ONE 13, e0199239. DOI:10.1371/journal.pone.0199239
(Smooth Cool Warm) Diverging Color Maps for Scientific Visualization. Kenneth Moreland. In Proceedings of the 5th International Symposium on Visual Computing, December 2009. DOI 10.1007/978-3-642-10520-3_9.
(Kindlmann) Face-based luminance matching for perceptual colormap generation. G. Kindlmann, E. Reinhard and S. Creem, IEEE Visualization, 2002. VIS 2002., 2002, pp. 299-306, DOI: 10.1109/VISUAL.2002.1183788.
(Turbo) Turbo, an improved rainbow colormap for visualization. Mikhailov, Anton. Google AI Blog 10 (2019): 15-16. URL: https://ai.googleblog.com/2019/08/turbo-improved-rainbow-colormap-for.html