In five minutes we are presenting our paper on arithmetic with color terms in the Harbour Room A here at EI. In case you did not make it, here are our slides:
Here are the speaker's notes:
{Color diagrams by scientists}
When color scientists draw chromaticity diagrams to illustrate their publications, they do it in black and white. Color, at most, is used for annotation purposes, not as self-reference.
{Color diagrams by designers}
This frustrates color designers, who make ample use of colors and expect more guidance from color scientists using the designer's language. Therefore, they prefer colorized chromaticity diagrams and they like how they can pencil in color theories because chromaticity diagrams can be used to predict the mixture of aperture color.
However, when they explain color theories using color terms, they get confused, because they have samples for the basic color terms and use them to label the colorized chromaticity diagram.
{sRGB color diagram}
In fact, the colorization of chromaticity diagrams is just that: a colorization. It bears little relation to the color's self-reference, because there is not device gamut covering the entire chromaticity diagram.
This figure shows how a chromaticity diagram can be more accurately colorized on an sRGB device.
{It is 3-dimensional!}
In the yellow range, the colorization fails and sticks out of the chromaticity diagram. This is because the gamut is tridimensional and the gamut rendering algorithm is somewhat imprecise. On the right side we move the viewpoint.
{Printer gamut}
You are seeing the projected image from a beamer, which in reality is not an sRGB device, so right now you are not seeing the correct self-referenced colors. In the case of a printer the error is even larger, but if for a moment you imagine viewing the diagram at right on a SWOP printer, you realized how off the colorized chromaticity diagram we saw earlier is.
{Color theories — an abstraction}
From a color science point of view, the various color theories have their own problems. Compare for example Leonardo's color circle with that proposed by Itten. The color are quite different. What happened?
{Itten revealed}
Of Leonardo we know that he considered color to be a perceived entity and his theory is based on color opponency. We discover Itten's method by looking at the black and white version of the color circle published in the student version of his book: He simply started with monolexemic color names and then interpolated with bilexemic color names to obtain a richer circle.
{Color by measurement}
We conclude, that we cannot predict arbitrary color mixtures by doing arithmetic with color terms obtained from colorized chromaticity diagrams. The general way to predict color mixtures is to reproduce actual samples and then measure them.
{Naming color}
How then can we determine the proper color terms? The World Color Survey has shown how to do it for the colors on the surface of the Munsell color gamut, and Boynton and Olson have shown how to do it for the full OSA color gamut.
Both experiment classes are limited to the basic color terms. For a large number of color terms, we need data from thousands of observers, which we can hardly do using controlled psychophysics techniques. Possible approaches include Amazon's Mechanical Turk and open Web surveys based on crowd-sourcing.
{Validating crowd-sourcing}
Unfortunately, non-withstanding Boynton's positive assessment of the uncontrolled protocol used in the World Color Survey, crowd-sourcing is still considered very controversial in the color science community. Therefore, we decided to perform a controlled experiment with an unusually large number of observers.
{Color chips given and color seating arrangement}
We printed a number of color chips and gave them to the researchers having lunch at the cafeteria and asked them to sit at the labelled tables with the term closest to their color chip.
{Calibrated lunch vs. Web color centroids}
As you can see from this diagram, we achieved excellent correlation between the controlled lunch room experiment and the uncontrolled crowd-sourced experiment on the Web. Thus, our data from the Web is scientifically valid.
{Beyond crowd-sourcing}
One question arising when teaching color design is which synonym to use for a given color. To answer this question we need more data than even crowd-sourcing can provide. Also, we have repeatedly written how color terms are ephemeral and change with time.
In this diagram we used Google's English book corpus and tracked the number times fuchsia and magenta appear in book each year from 1800 to 2000. The appearance of the term magenta coincides with the historical facts and the numbers suggest your should prefer the term magenta over fuchsia.
{What is the best term for RGB = (0, 1, 1)?}
This diagram illustrates how cyan is not a good choice and you should use the term turquoise instead.
{Is the term for RGB = (1, 1, 0) monolexemic?}
We can use this approach also to answer very difficult questions, like the number of basic color terms. This diagram illustrates that olive green might be becoming a basic term also in English, because chartreuse is monolexemic.
Our data shows that few people ever learned to correctly spell fuchsia, but the data shows this is not critical. However, you should definitely learn how to spell chartreuse.
