Turbo, an Improved Rainbow Colormap for Visualization (ai.googleblog.com)

Article summary

The article introduces Turbo, a new colormap designed to improve upon the traditional Jet colormap by addressing its shortcomings such as false detail, banding, and color blindness ambiguity. Turbo mimics the lightness profile of Jet but with a more perceptually uniform color distribution. The authors claim that Turbo enhances detail and makes it easier to disambiguate low and high ends of the data range. The colormap is designed to work well for a variety of data visualization tasks.

Main themes

• Color Maps
• Data Visualization
• Perceptual Uniformity
• Color Blindness
• Visualization Tools
• Design Principles

What commenters say

• Turbo's non-linear lightness profile makes it less suitable for certain types of data visualization, particularly those requiring precise comparisons of small differences.
• The use of Turbo's non-linear colormap can be beneficial for conveying qualitative information and highlighting differences in specific regions of the data.
• The desirable properties of Jet, such as its ability to enhance detail, are not sufficient to justify its use, given its numerous shortcomings.
• The development of Turbo did not adequately consider the needs of colorblind individuals, and its colorblind-friendliness is limited.
• The choice of colormap depends on the specific use case and the type of data being visualized, with different colormaps suited to different tasks.
• Turbo's design process was not principled or careful, and its similarity to Jet is a drawback rather than a benefit.
• The introduction of Turbo provides an additional option for data visualization, and its unique properties make it useful for certain types of visualizations.