Standardization of Colors

Color theory is a very complex problem that color scientists have wrestled with for years. How can you accurately generate and replicate specific colors present in sunlight? How can you possibly be sure that you would get the same results every time?

Like many industries, the color science community turned to the standardization process. To study predictable and reproducible color blending, the International Commission on Illumination, or Commission Internationale de l’Eclairge (CIE), noted the effects of observing blends of three primary colors at specific frequencies: red (700 nm), green (546.1 nm), and blue (435.8 nm). The CIE selected these particular colors and wavelengths because, in various combinations and intensities, they can form many, but not all, colors. Combining equal amounts of these colors in pairs produces three secondary colors: cyan (green + blue), magenta (red + blue), and yellow (red + green).

Combining the designated primary or secondary colors in the correct proportions produces a visual sensation indistinguishable from equi-energy white light. However passing this prism reveals that the light does not contain all spectral colors, only red, blue, and green.

Subtractive Color

Where additive color creates color by adding light of different wavelengths, subtractive color absorbs and reflects back light to create color.

Colorants (paint or ink, for example), when applied to a surface (the media) and viewed using a particular light source, impart hues by absorbing, or subtracting, some light frequencies before reflecting others from the surface. This process of absorbing some frequencies depending on the light conditions is called subtractive light.

Subtractive primary colors are different from additive primary colors. The subtractive light primary colors are cyan, magenta, and yellow. In subtractive light, the secondary colors are red (magenta + yellow), green (cyan + yellow), and blue (cyan + magenta). Notice that the primaries and secondaries for subtractive color are the opposite of those for additive color.

It is for this and other complex reasons that images displayed on computer screens using additive color may not look the same when printed using subtractive color without considerable forethought and intervention.

This returns us to the car showroom example mentioned at the chapter beginning. The paint on that car will never look exactly the same unless you look at it under the exact same light conditions, because different lights have different spectral compositions.

Metamers

Colored objects that match under one light source (sunlight, for example) but differ under another (perhaps a street light) are called metamers. The disappointing results of a car purchase or paint touch-up may be a metameric consequence, and there are many more examples. Makeup mirror manufacturers capitalize on this phenomenon by providing selectable mirror lights labeled “fluorescent,” “inside,” and “outside.” This enables the person applying makeup to select the optimum makeup with respect to the anticipated lighting.

Before moving on to the human eye and how our visual system works, here are the salient things to remember about light. First, the way in which we perceive a color is a direct result of the effects of light on its surface. Things may not always be what they seem and they may not always look identical under different light conditions. Second, additive color is the combination of spectral lights that are added together to create hundreds of colors, pastels, and hues. With subtractive color, the ink or paint absorbs some light frequencies and reflects others back.

Having considered the complications of color sources, you must now consider the human factors involved. The interaction between the eye and the brain is complex; what the eye sees, the brain may not. The next step in building this framework for the problems that you may encounter in digital image processing is to take a closer look at how the eye works. I intend to be brief here because you bought this book to learn about image processing, not human biology!