The current mathematical models used for color perceptions are incorrect and require a “paradigm shift.”
, is the work of the lead author and computer scientist Roxana Bujack and a research team from the, Bujack, who creates scientific visualizations at Los Alamos National Laboratory, called the current mathematical models used for color perceptions incorrect and requiring a “paradigm shift.”Being able to accurately model human color perception has a tremendous impact on automating image processing, computer graphics, and visualization.
What the team was surprised to find is that they were the first ones to realize the established practice of applying “Riemannian geometry” to the 3D space didn’t work. This happens due to the effect of “diminishing returns” where “large color differences are perceived as less than the sum of small differences,” the scientists wroteIn other words, a big difference in color is perceived to be less than the sum of small differences in color that lie between two widely separated shades. The researchers demonstrated that this effect cannot be accounted for in a Riemannian geometry.
“If we are lucky, a Riemannian space with a scaling function could do the trick, but more experiments are needed to see if that works,” she added.“But of course, you have to allow for some perceptual 'noise' like in the theory of Thurstone. Without the stochastical component, it would violate the most fundamental metric property: the identity of indiscernibles, i.e., that zero is only returned if both inputs are identical.
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