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Introduction to Color. Spectral color: single wavelength (e.g., from laser); “ROY G. BIV” spectrum Non-spectral color: combination of spectral colors; can be shown as continuous spectral distribution or as discrete sum of n primaries (e.g., R, G, B); most colors are non-spectral mixtures

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Spectral color: single wavelength (e.g., from laser); “ROY G. BIV” spectrum

Non-spectral color: combination of spectral colors; can be shown as continuous spectral distribution or as discrete sum of n primaries (e.g., R, G, B); most colors are non-spectral mixtures

Metamers are spectral energy distributions that are perceived as the same “color”

each color sensation can be produced by an arbitrarily large number of metamers

Energy Distribution and Metamers

White light spectrum where the height of the curve is the spectral energy distribution

Cannot predict average observer’s color sensation from a distribution


Metamers (1/4)

Different light distributions that produce the same response

  • Imagine a creature with just one kind of receptor, say “red,” whose response curve looks like this:
  • How would it respond to each of these two light sources?
  • Both signals will generate the same amount of “red” perception. Hence they are metamers
    • one type of receptor can never give us more than one color sensation, red in this case
    • although, different signals may result in different perceived brightness

Metamers (2/4)

  • Consider a creature with two receptors (R1, R2)
  • Note that in principle an infinite number of frequency distributions can simulate the effect of I2, e.g., I1
    • in practice, for In near base of response curves, amount of light required becomes impractically large





Metamers (3/4)

  • For three types of receptors, there are potentially infinitely many color distributions (metamers) that will generate exactly the same sensations
    • you can test this out with the metamer applet
    • we’ll do this in lab
  • Conversely, no two spectral (monochromatic) lights can generate identical receptor responses and therefore all look unique
  • Thomas Young in 1801 postulated that we need 3 types of receptors to distinguish the gamut of colors represented by triples H, S and V

Metamer Applet


Metamers (4/4)

  • For two light sources to be metamers, the amounts of red, green, and blue response generated by the two sources must be identical
  • This amounts to three constraints on the lights
  • But the light sources are infinitely variable – one can adjust the amount of light at any possible wavelength…
  • So there are infinitely many metamers
  • Observations:
    • if two people have different response curves, the metamers for one person will be different from those for the other
    • metamers are purely perceptual: scientific instruments can detect the difference between two metameric lights
    • even a prism can help show how two metameric lights are different…
Spectral color: single wavelength (e.g., from laser); “ROY G. BIV” spectrum

Non-spectral color: combination of spectral colors; can be shown as continuous spectral distribution or as discrete sum of n primaries (e.g., R, G, B); most colors are non-spectral mixtures

Metamers are spectral energy distributions that are perceived as the same “color”

each color sensation can be produced by an arbitrarily large number of metamers

Energy Distribution and Metamers

White light spectrum where the height of the curve is the spectral energy distribution

Cannot predict average observer’s color sensation from a distribution

Can characterize visual effect of any spectral distribution by triple (dominant wavelength, excitation purity, luminance):

Dominant wavelength corresponds to hue we see; spike of energy e2

Excitation purity is the ratio of monochromatic light of the dominant wavelength and white light to produce the color

e1 = e2, excitation purity is 0% (unsaturated)

e1 = 0, excitation purity is 100% (fully saturated)

Luminance relates to total energy, which is proportional to integral of the product of distribution and eye’s response curve (“luminous efficiency function”) – depends on both e1 and e2


dominant wavelength of a real distribution may not be the one with the largest amplitude!

some colors (e.g., purple) have no dominant wavelength


Dominant Wavelength

Energy Density


Idealized uniform distribution except for e2

wavelength, nm



Colorimetry Terms

Receptors in retina (for color matching)

rods and three types of cones (tristimulus theory)

primary colors (only three used for screen images: approximately red, green, blue (RGB))

Note: the 3 types of receptors each respond to a wide range of frequencies, not just three spectral primaries

Opponent channels (for perception)

other cells in retina and neural connections in visual cortex

blue-yellow, red-green, black-white

4 psychological color primaries*: red, green, blue, and yellow

Opponent cells (also for perception)

spatial (context) effects, e.g., simultaneous contrast, lateral inhibition

* These colors are called “psychological primaries” because

each contains no perceived element of the others regardless of

intensity. (

Three Layers of Human Color

Perception – Overview

Receptors contain photopigments that produce electro-chemical response; our dynamic range of light, from a few photons to looking at the sun, is 1011 => division of labor among receptors

Rods (scotopic): only see grays, work in low-light/night conditions, mostly in periphery

Cones (photopic): respond to different wavelength to produce color sensations, work in bright light, densely packed near fovea, center of retina, fewer in periphery

Young-Helmholtz tristimulus theory1: 3 types of cones, each sensitive to all visible wavelengths of light, each maximally responsive in different ranges, often associated with red, green, and blue (although red and green peaks of these cones actually more yellow)

The three types of receptors can produce a 3-space of hue, saturation and value (lightness/brightness)

To avoid misinterpretations S (short), I (intermediate), L (long) often used instead

Receptors in Retina

1Thomas Young proposed the idea of three receptors in 1801. Hermann von Helmholtz looked at the theory from a quantitative basis in 1866. Hence, although they did not work together, the theory is called the Young-Helmholtz theory since they arrived at the same conclusions.

Tristimulus theory does not explain color perception, e.g., not many colors look like mixtures of RGB (violet looks like red and blue, but what about yellow?)

Triple Cell Response Applet:

Tristimulus Theory

Spectral-response functions of fλ each of the three types of cones on the human retina

Additional neural processing

the three receptor elements have both excitatory and inhibitory connections with neurons higher up that correspond to opponent processes; one pole of each activated by excitation, the other by inhibition

All colors can be described in terms of 4 “psychological color primaries” R, G, B, and Y

However, a color is never reddish-greenish or bluish-yellowish: leads to idea of two “antagonistic” opponent color channels, red-green and yellow-blue

a third, black and white channel, relays lightness information




- + +

+ + +

+ - +




Hering’s Chromatic Opponent Channels

Light of 450 nm

Each channel is a weighted sum of receptor outputs – linear mapping

Hue: Blue + Red = Violet

Some cells in the opponent channels are also spatially opponent, a type of lateral inhibition (called double-opponent cells)

Responsible for effects of simultaneous contrast and after images

green stimulus in cell surround causes some red excitement in cell center, making gray square in field of green appear reddish


color perception strongly influenced by context, training, etc., not to mention abnormalities such as various types of color blindness

Spatially Opponent Cells

Nature provides for contrast enhancement at boundaries between regions: edge detection. This is caused by lateral inhibition.

Mach Bands and Lateral Inhibition


Stimulus intensity


Position on stimulus


Lateral Inhibition and Contrast

Two receptor cells, A and B, are stimulated by neighboring regions of a stimulus. A receives an intense amount of light. A’s excitation serves to stimulate the next neuron on the visual chain, cell D, which transmits the message further toward the brain. But this transmission is impeded by cell B, whose own intense excitation exerts an inhibitory effect on cell D. As as result, cell D fires at a reduced rate. (Excitatory effects are shown by green arrows, inhibitory ones by red arrows.) Intensity of cell cj=I(cj) is a function of cj’s excitation e(cj) inhibited by its neighbors with attenuation coefficients ak that decrease with distance. Thus,

At the boundary more excited cells inhibit their less excited neighbors even more and vice versa. Thus, at the boundary the dark areas are even darker than the interior dark ones, and the light areas are lighter than the interior light ones.


Color Matching

  • Tristimulus theory leads to notion of matching all visible colors with combinations of “red,” “green,” and “blue” mono-spectral “primaries;” it almost works
  • Note: these are NOT response functions!
  • Negative value of => cannot match, must “subtract,” i.e., add that amount to unknown
  • Note that mixing positive amounts of arbitrary R, G, B primaries provides large color gamut, e.g., CRT, but no device based on a finite number of primaries can show all colors!

Wavelength λ (nm)

Color-matching functions, showing the amounts of the three primaries needed by the average observer to match a color of constant luminance, for all values of dominant wavelength in the visible spectrum.


CIE Space for Color Matching

  • Commission Internationale de l´Éclairage (CIE)
  • Defined X, Y, and Z primaries to replace red, green and blue primaries

=> RGBinXYZi via a matrix

The mathematical color matching functions xλyλ, and zλ for the 1931 CIE X, Y, and Zprimaries. They are defined tabularly at 1 nm intervals for color samples that subtend 2° field of view on retina

Note the irregular shape of the visible gamut in CIE space; it is

due to the eye's response as measured by the response curves

The range of colors which can be displayed on the monitor

is clearly smaller than all colors visible in XYZ space.

CIE Space Showing an RGB


The color gamut for a typical color monitor with the XYZ color space

Left: the plane embedded in CIE space.

Top right: a view perpendicular to the plane.

Bottom right: the projection onto the (X, Y) plane

(that is, the Z = 0 plane), which is called the chromaticity diagram

Chromaticity – Everything that deals (H, S) with color, not


For an interactive chromaticity diagram, check out the following


CIE Space Projection to Chromaticity Diagram

Several views of the X + Y+ Z = 1 plane of the CIE space

Amounts of X, Y, and Z primaries needed to match a color with a spectral energy distribution P(λ), are:

in practice use S’s

Then for a given color C, C = XX + YY + ZZ

Get chromaticity values that depend only on dominant wavelength (hue) and saturation (purity), independent of the amount of luminous energy, for a given color, by normalizing for total amount of luminous energy = (X + Y + Z)

thus x + y + z = 1; (x,y,z) lies on the X + Y + Z = 1 plane

CIE Chromaticity Diagram (1/5)

(x, y) determinesz but cannot recover X, Y, Z from only x and y. Need one more piece of information, typically Y, which carries luminance information


CIE Chromaticity Diagram (2/5)

CIE chromaticity diagram is the projection onto the (X, Y) – plane of the (X + Y + Z ) = 1 plane

It plots x and y for all visible chromaticity values:

all perceivable colors with same chromaticity but different luminances map into same point

100% spectrally pure, monochromatic colors are on curved part

since luminance factored out, colors that are luminance-related are not shown,

e.g., brown = orange-red chromaticity at low luminance

infinite number of planes which project onto (X + Y + Z = 1) plane and all of whose colors differ; thus it is NOT a full color palette!

illuminant C: near (but not at) x = y = z = 1/3; close to daylight

CIE Chromaticity Diagram (3/5)

spectral locus

not the spectral locus

Uses of CIE Chromaticity Diagram:

Name colors

Define color mixing

Define and compare color gamuts

CIE Chromaticity Diagram (4/5)


CIE Chromaticity Diagram (5/5)

CIE 1976 UCS chromaticity diagram from Electronic Color: The Art of Color Applied to Graphic Computing by Richard B. Norman, 1990

Inset: CIE 1931 chromaticity diagram

Measure dominant wavelength and excitation purity of any color by matching with mixture of the three CIE primaries:

colorimeters measure tristimulus X, Y, and Z values from which chromaticity coordinates are computed

spectroradiometers measures the wavelengths of both spectral energy distribution and tristimulus values

Suppose matched color is at point A in figure below:

when two colors added, new color lies on straight line connecting two colors => A = tC + (1– t)B where B is a spectral color

ratio AC/BC is excitation purity of A

Using the CIE Chromaticity

Diagram (1/2)

Colors portrayed on the chromaticity diagram

Complementary colors can be mixed to produce white light (a decidedly non-spectral color!); white can thus be produced by an approximately constant spectral distribution as well as by only two complementary colors, e.g., greenish-blue, D, and reddish-orange,E (on previous slide).

Some nonspectral colors (colors not on the spectral locus, like G) cannot be defined by dominant wavelength; defined by complementary dominant wavelength

Using the CIE Chromaticity

Diagram (2/2)

As we saw, any two colors, I and J, can be added to produce any color along their connecting line by varying relative amounts of two colors in mix

Third color K can be used with various mixes of I and J to produce a gamut of all colors in triangle IJK, again by varying relative amounts

Shape of diagram shows why visible red, green and blue cannot be additively mixed to match all colors; no triangle whose vertices are within the visible area can completely cover visible area (e.g., purple and magenta)

matching of all visible colors requires negative amounts of primaries for some

due to the way response curves overlap and sum

Color Gamuts (1/2)

Mixing colors


Color Gamuts (2/2)

  • Smallness of print gamut with respect to color monitor gamut => faithful reproduction by printing must use reduced gamut of colors on monitor
No gamut described by a linear combination of n physical (real) primaries (yielding aconvex hull) can simulate the eye’s responses to all visible colors

Review of CIE

shape of CIE space determined by the Matching Experiment: subject is shown a color and asked to create a metameric match out of three colored monochromatic primaries, R, G and B

most colors can be matched, but some can’t because of the way the response curves are overlapped

would need “negative amounts” of one of the primaries in order to match all visible color samples; not physically possibly, but can be simulated by adding that color to the sample to be matched.

to simplify, the CIE mathematical primaries, X, Y, and Z, are used to get all positive color matching functions

Let’s see in another way why 3 (indeed n) physical primaries aren’t sufficient to match an arbitrary color by looking at the response function

Why the Chromaticity Diagram is

Not Triangular

Here we try to match a monospectral cyanish color (440nm) with our three red, green, and blue lights. We show the three response function, labeled S, I, and L and try two different R, G, B intensity triples

Match Failure Example

try to match spectral light at 440 nm

Three R, G, B primaries

Height of bar shows amplitude of primary

Taken from Falk’s Seeing the Light, Harper and Row, 1986

Chromatic Opponent Channels on

Chromaticity Diagram

Purpose: specify colors in some gamut

Since gamut is a subset of all visible chromaticities, model does not contain all visible colors

3D color coordinate system subset containing all colors within a gamut

Means to convert to other model(s)

Example color model: RGB

3D Cartesian coordinate system

unit cube subset

Use CIE XYZ space to convert to and from all other models

Color Models for Raster Graphics


Hardware-oriented models: not intuitive – do not relate to concepts of hue, saturation, brightness

RGB, used with color CRT monitors

YIQ, the broadcast TV color system

CMY (cyan, magenta, yellow) for color printing

CMYK (cyan, magenta, yellow, black) for color printing

User-oriented models

HSV (hue, saturation, value) – also called HSB (hue, saturation, brightness)

HLS (hue, lightness, saturation)

The Munsell system


Color Models for Raster Graphics


RGB primaries are additive:

The RGB cube (Grays are on the dotted main diagonal)

Main diagonal => gray levels

black is (0, 0, 0)

white is (1, 1, 1)

RGB color gamut defined by CRT phosphor chromaticities

differs form one CRT to another

The RGB Color Model (1/3)

Conversion from one RGB gamut to another

convert one to XYZ, then convert from XYZ to another

check out for some good examples of this

Form of each transformation:

Where Xr, Xg, and Xb are the weights applied to the monitor’s RGB colors to find X, and so on

M is the 3 x 3 matrix of color-matching coefficients

Let M1 and M2 be matrices to convert from each of the two monitor’s gamuts to CIE

M2-1 M1 converts form RGB of monitor 1 to RGB of monitor 2

The RGB Color Model (2/3)

But what if

C1 is in the gamut of monitor 1 but is not in the gamut of monitor 2, i.e., C2 = M2-1 M1C1 is outside the unit cube and hence is not displayable?

Solution 1: clamp RGB at 0 and 1

simple, but distorts color relations

Solution 2: compress gamut on monitor 1 by scaling all colors from monitor 1 toward center of gamut 1

ensure that all displayed colors on monitor 1 map onto monitor 2

The RGB Color Model (3/3)

Used in electrostatic and in ink-jet plotters that deposit pigment on paper

Cyan, magenta, and yellow are complements of red, green , and blue

Subtractive primaries: colors are specified by what is removed or subtracted from white light, rather than by what is added to blackness

Cartesian coordinate system

Subset is unit cube

white is at origin, black at (1, 1, 1):



(minus blue)

(minus red)






(minus green)

The CMY(K) Color Model (1/2)

  • subtractive primaries (cyan, magenta, yellow) and their mixtures
Color printing presses, some color printers use CMYK (K=black)

K used instead of equal amounts of CMY

called undercolor removal

richer black

less ink deposited on paper – dries more quickly

First approximation – nonlinearities must be accommodated:

K = min(C, M, Y)

C’ = C – K

M’ = M – K

(one of C’, Y’, M’ will be 0)

The CMY(K) Color Model (2/2)

Recoding for RGB for transmission efficiency and downward compatibility with B/W broadcast TV; used for NTSC (National Television Standards Committee (cynically, “never the same color”))

Y is luminance – same as CIE Y primary

I and Q encode chromaticity

Only Y = 0.3R + 0.59G + 0.11B shown on B/W monitors:

weights indicate relative brightness of each primary

assumes white point is illuminant C:

xw = 0.31, yw = 0.316, and Yw = 100.0

Preparing color material which may be seen on B/W broadcast TV, adjacent colors should have different Y values

NTSC encoding of YIQ:

4 MHz Y (eye most sensitive to r luminance)

1.5 MHz I (small images need only 1 color dimension)

0.6 MHz Q

The YIQ Color Model

Hue, saturation, value (brightness)

Hexcone subset of cylindrical (polar) coordinate system

Single hexcone HSV color model. (The V = 1 plane contains the RGB model’s R = 1, G = 1, B = 1, in the regions shown)

Has intuitive appeal of the artist’s tint, shade, and tone model

pure red = H = 0, S = 1, V = 1; pure pigments are (I, 1, 1)

tints: adding white pigment n decreasing S at constant V

shades: adding black pigment n decreasing V at constant S

tones: decreasing S and V

The HSV Color Model (1/2)

Colors on V = 1 plane are not equally bright

Complementary colors 180° opposite

Saturation measured relative to color gamut represented by model which is subset of chromaticity diagram:

therefore, 100% S 100% excitation purity

Top of HSV hexcone is projection seen by looking along principal diagonal of RGB color

RGB subcubes are plane of constant V

Code for RGB n HSV on page 592, 593

Note: linear path RGB linear path in HSV!

The HSV Color Model (2/2)

RGB color cube viewed along the principal diagonal

Hue, lightness, saturation

Double-hexcone subset

Maximally saturated hues are at S = 1, L = 0.5

Less attractive for sliders or dials

Neither V nor L correspond to Y in YIQ!

Conceptually easier for some people to view white as a point

The HLS color Model

They are perceptually non-uniform

move through color space from color C1 to a new color C1΄through a distance ΔC C1΄ = C1 + ΔC

move through the same distance ΔC, starting from a different color C2 C2΄ = C2 + ΔC

the change in color in both cases is mathematically equal, but is not perceived as equal

Moving a slider almost always causes a perceptual change in the other two parameters, which is not reflected by changes in those sliders; thus, changing hue frequently will affect saturation and value, even in Photoshop!

Ideally want a perceptually uniform space: two colors that are equally distant are perceived as equally distant, and changing one parameter does not perceptually alter the other two

Historically, the first perceptually-uniform color space was the Munsell system

Problems with Standard

Color Systems

Created from perceptual data, not as a transformation of or approximation to CIE

Uniform perceptually-based 3D space

accounts for the fact that a bright yellow is much lighter than a bright blue, and that many more levels of saturation of blue can be distinguished than of yellow

Magnitude of change in one parameter always maps to the same effect on perception

Hues arranged on a circle

a 20 degree rotation through this circle always causes the same perceptual change, no matter where on the circle you start from

does not cause changes in saturation or value

Saturation as distance from center of circle

moving away from the center a certain distance always causes the same perceptual change

does not cause changes in hue or value

Value as height in space

moving vertically always causes the same perceptual change

does not cause changes in hue or saturation

Basis for Graphics Group research during 1999-2001 into new color pickers (was led by ams and funded by Adobe)

The Munsell System

CIE Lab was introduced in 1976

popular for use in measuring reflective and transmissive objects

Three components:

L* is luminosity

a* is red/green axis

b* is yellow/blue axis

Mathematically described space and a perceptually uniform color space

Given white = (Xn, Yn, Zn)

These transformations cause regions of colors perceived as identical to be of the same size



+ Cartesian coordinate system

+ linear

+ hardware-based (easy to transform to video)

+ tristimulus-based

- hard to use to pick and name colors

- doesn’t cover gamut of perceivable colors

- non-uniform: equal geometric distance => unequal

perceptual distance


+ covers gamut of perceived colors

+ based on human perception (matching experiments)

+ linear

+ contains all other spaces

- non-uniform (but variations such as CIE Lab are closer

to Munsell, which is uniform)

- xy-plot of chromaticity horseshoe diagram doesn’t

show luminance

Color Model Pros and Cons (1/2)

Example: Photoshop Lab color model is based on CIE Lab space

+ based on psychological colors (y-b, r-g, w-b)

- terrible interface in Photoshop

- no visualization of the color space

- very difficult to determine what values mean if you are unfamiliar with the space

- picks colors which are out of the print gamut

- primarily used as an internal space to convert between



+ easy to convert to RGB

+ easy to specify colors

- nonlinear

- doesn’t cover gamut of perceivable colors

- nonuniform

Color Model Pros and Cons (2/2)

English-language names

ambiguous and subjective

Numeric coordinates in color space with slide dials:

Interactive Specification of Color


Left: Lab color picker from Adobe Photoshop for Windows

Below: RGB color picker from Adobe Photoshop for Mac OS

Interact with visual representation of the color space

Important for user to see actual display with new color

Beware of surround effect!

Interactive Specification of Color


HSB color picker from Adobe Photoshop

HSV color picker from Mac OS X’s Finder

Interpolation needed for:

Gouraud shading


blending images together in a fade-in, fade-out sequence

Results depend on the color model used:

RGB, CMY, YIQ, CIE are related by affine transformations, hence straight line (i.e., interpolation paths) are maintained during mapping

not so for HSV, HLS; for example, interpolation between red and green in RGB:

interpolating in HSV:

midpoint values in RGB differ by 0.5 from same interpolation in HSV:

(60º, 1, 0.5)(60º, 1, 1)

Interpolating in Color Space (1/2)

  • red = (1, 0, 0), green = (0, 1, 0)
  • midpoint = (0.5, 0.5, 0)
  • red = (0º, 1, 1); green = (120º, 1, 1)
  • midpoint = (60º, 1, 1)
  • RGB_to_HSV = (60º, 1, 0.5)
RGB, red is (1, 0, 0) and cyan is (0, 1, 1) which interpolate to (0.5, 0.5, 0.5), gray

in HSV, that is (UNDEFINED, 0, 0.5)

In HSV, red is (0º, 1, 1) and cyan is (180º, 1, 1) which interpolates to (90º, 1, 1)

new hue at maximum value and saturation, whereas the “right” result of combining equal amounts of complementary colors is a gray value

Thus, interpolating, transforming transforming, interpolating

For Gouraud shading (see Rendering unit), use any of the models because interpolants are generally so close together that interpolation paths are close together

For blending two images, as in fade-in fade-out sequence or for antialiasing, colors may be quite distant

use additive model, such as RGB

If interpolating between two colors of fixed hue (or saturation), maintain fixed hue (saturation) for all interpolated colors by HSV or HLS

note fixed-saturation interpolation in HSV or HLS is not seen as having exactly fixed saturation by viewer!

Interpolating in Color Space (2/2)


Using Color in

Computer Graphics

  • Aesthetic uses:
    • establish a tone or mood
    • promote realism
  • Highlight
  • Code numeric quantities
    • temperature across the U.S.
    • vegetation and mineral concentrations on Earth, moon, and planets (LandSat, Mars missions)
    • speed of fluids in computational fluid dynamics (streamlines)
Careless use of color is perilous

in experiment, color reduced user performance by one-third

Decorative use of color subservient to functional use:

test with real users

provide “best judgment” defaults

allow user to override defaults

design first for a monochrome display (color use is redundant in monochrome displays and for color-blind users)

Using Color in Computer

Graphics: Functionality

Color harmony:

select colors according to some method, typically by traversing a smooth path in a color model

restrict colors to planes or hexcones in a color space

space colors at equal perceptual distances

light-dark contrast more important than hue contrast


if a chart contains just a few colors, the complement of one of the colors should be used as the background

use neutral (gray) background for an image with many colors

separate non-harmonious colors by thin black border


be redundant (“dual coding”)

show reference scale

color can carry unintended meanings:

bright, saturated colors stand out (may give unintended emphasis)

display elements of same color may incorrectly be associated by user

Using Color in Computer

Graphics Approach

Eye is more sensitive to spatial variation

fine detail should vary from the background not just in chromaticity, but in brightness

Blue and black, yellow and white are particularly bad combinations

don’t use blue for text

For color-blind users, avoid reds and greens with low saturation and luminance

Color of small objects less than 20-40 minutes of arc are not distinguishable

Using Color in Computer Graphics:

Physiological Rules (1/2)

This is hard to read.

So is this.

And this, too! Yikes!

Difficult to perceive color when used with small objects => don’t use color coding for them

Perceived color of object is affected by color of surrounding area

Strongly saturated colors produce after images

Color affects perceived size

red seen as larger than green

colors refract differently through our lens: appear at different depths, e.g., red (closer) vs. blue (farther) strongest effect

Apply color conservatively, not gratuitously –

“color-itis” as bad as “font-itis”

Using Color in Computer Graphics:

Physiological Rules (2/2)

To describe the character of light (ignoring phase) requires an infinity of amplitude – wavelength data – light spectra lie in an infinite-dimensional space

Human perception, below certain intensity levels, is not at all linear

At reasonable levels light perception is log-linear; displays also not linear, influences color-table design

“Less is More” in judicious use of color – use some simple guidelines based on aesthetics and psychophysics

Take Away Ideas

  • If you are interested in learning more about color, check out Anne Morgan Spalter’s book, The Computer in the Visual Arts, available on

For More Information

For more on color, check out the following web sites:

  • Color Matters
  • Educational Color Applets
  • Gamma FAQ and Color FAQ
  • More Frequently Asked Questions about color
    • info on color blindness and the way we interpret color
  • Comprehensive List of Color Models
  • Online book on color
  • Map Coloring
  • Get your own color cube!!!
  • Excerpt from Thomas Young’s paper