Theodore E. Meyer

Light Spectra to RGB

To understand color, you have to understand three “P”s: the physiology of the eye, the physics of converting light spectra to red, green, and blue (RGB) signals, and finally the perception of the RGB signals to color hue, saturation, and intensity. We covered the physiology part in Chapter 2. In this chapter, we cover the physics: how exactly are light spectra converted into the RGB signals? We cover perception in Chapter 4.

Eye, Ear, and Brain

To understand color, we must first discuss the eye. And to understand the eye, we must first discuss the ear.Why? Because both the eye and the ear are remote sensing devices that give us information about our environment. There are many deep similarities between the two, and also some fundamental differences. These similarities and differences will help us come to a deeper understanding of both. Our focus here is the spatial, spectral, and temporal resolution of the information gained by the two senses.

Numbers In Computers

In this book, we are concerned only with color information that can be quantified or represented as numbers. How should colors be coded in the computer? Computers only understand ones and zeros; they most certainly do not understand the color “baby blue” directly. So colors must first be turned into numbers before talking to a computer.

Defining Colors—The CIE Color Diagram

In the previous chapters, color has always been defined in terms of three numbers, or equivalently, a position in 3-space. But defining colors in terms of 3-space coordinates is a bit awkward: it is difficult to publish a 3-space chart in a book, for example. Is there a way to describe colors in terms not of 3-space coordinates, but of a flat, 2-space coordinate?

Reproducing Colors—Technologies

In the previous chapter, we talked about the fundamentals of color image reproduction technology, such as what a pixel is, how to evaluate resolutions, and especially how to reproduce continuous tone images with discrete tone technologies.

Color by Numbers—Using Color to Visualize Data

In the previous chapter, we talked about index color images as approximations of the appearance of a true color image. In this chapter, we show how we can use all this color image technology to use color to visualize data; to “Color by Numbers,” so to speak.

Color in Computers—Fundamentals

In the previous chapter we talked in general about how numbers can be organized and stored in computers. Not much was said about using those numbers for storing colors. In this chapter, that is all we talk about: how do we encode particular colors in computers, how do we store color images in computers, and so on.

Defining Colors—Color Models

In the last several chapters, we have discussed in some depth three ways to describe colors: using red, green, and blue (RGB), using opponent colors (Intensity, Blue-Yellow, Red-Green), and the hue-saturation-intensity model (HSl). These color models were used to describe the physics, physiology, and perception of color vision.

Hue, Saturation, Intensity

In Table 4.1 we list several different ways to describe, produce, and manipulate color. It is an unfortunate fact of life that you need a nodding understanding of all these different color description models if you want to use color effectively.

Reproducing Colors—Fundamentals

How do we reproduce a color image? That is what this chapter and the next are about: the various technologies we have at our disposal to recreate a color experience. None of these technologies is perfect. Perfection in this case means being able to generate a five-dimensional (three spatial dimensions, one spectral dimension, and time) datastream of several gigabaud1 that is indistinguishable from reality. Well, nobody has the technology to do that, at least not yet.