A team from Los Alamos National Laboratory has finalized a theory on color perception proposed by Erwin Schrödinger nearly a century ago. Using advanced geometry, researchers defined key elements like the neutral axis, showing that hue, saturation, and lightness stem from the inherent structure of color vision. Their work addresses longstanding flaws and enhances applications in visualization science.
In the 1920s, physicist Erwin Schrödinger outlined a mathematical framework for understanding how humans perceive color, building on Bernhard Riemann's 19th-century ideas of curved perceptual spaces. Human color vision relies on three types of cone cells in the eye, sensitive to red, blue, and green light, which scientists represent in three-dimensional color spaces. For decades, Schrödinger's model influenced color science, but gaps persisted, particularly in defining the neutral axis—the line of gray tones from black to white.
Roxana Bujack, a scientist at Los Alamos National Laboratory, led a team that refined this theory by applying geometry to describe hue, saturation, and lightness precisely. Their findings, presented at the Eurographics Conference on Visualization, demonstrate that these qualities arise from the internal structure of the color system, not external factors like culture or experience. "What we conclude is that these color qualities don't emerge from additional external constructs such as cultural or learned experiences but reflect the intrinsic properties of the color metric itself," Bujack said. This metric encodes the perceived distance between colors, or how different two colors appear to an observer.
A major achievement was establishing the neutral axis purely from the geometry of the color metric, extending beyond the traditional Riemannian framework. The team also corrected the Bezold-Brücke effect, where increased brightness shifts perceived hue, by calculating the shortest path in the geometric space rather than assuming straight lines. They similarly addressed diminishing returns in color differences, where larger separations become less noticeable.
This research builds on a 2022 paper in the Proceedings of the National Academy of Sciences and was published in Computer Graphics Forum in 2025. Funded by Los Alamos's Laboratory Directed Research and Development program and the National Nuclear Security Administration's Advanced Simulation and Computing program, the work supports visualization in fields like photography, video, data analysis, and national security simulations.
