By Roxana Bujack
The opportunity to correct the work of Nobel Prize-winning physicist Erwin Schrödinger — yes, that Schrödinger, of quantum cat fame — comes once in a lifetime, so when my colleagues and I discovered he and others were wrong in their mathematical description of how people perceive color, we jumped on it.
In my early-career research project, our scientific visualization team at Los Alamos National Laboratory wanted to develop algorithms to automatically improve the color maps underlying the images and movies that make data — numbers — easy to understand and interpret at a glance. Our minds have trouble digging meaning out of arrays of digits, but we can quickly spot patterns and trends when those numbers are converted to pictures, with colors representing different values in the data. Our visualizations help physicists, climate modelers, space weather researchers and many others make sense of vast data streams that might otherwise bury their revelations beneath seemingly endless spreadsheet columns.
Interpreting data in a visualization depends strongly on the quality of the color map, which assigns colors to data values. A continuous color map follows a path through color space. We thought we could automate the design of color maps if we treated them as pure geometric objects and mathematically captured what makes a good colormap. For that to work, you have to have a color model that really captures human perception. We wanted to use the century-old model developed by Schrödinger and others.
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