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Color. CS5600 Computer Graphics by Rich Riesenfeld Spring 2006. Lecture Set 11. Color Issues. Physical nature of color Eye mechanism of color Rods, cones, tri-stimulus model Brain mechanism of color Color spaces Aesthetic and physiological. Color. Color is complicated! - PowerPoint PPT Presentation
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CS5600 Computer Graphics
by
Rich Riesenfeld
Spring 2006
Lect
ure
Set
11
Spring 2006 Utah School of Computing 2
• Physical nature of color
• Eye mechanism of color
–Rods, cones, tri-stimulus model
• Brain mechanism of color
• Color spaces
• Aesthetic and physiological
Spring 2006 Utah School of Computing 3
• Color is complicated!–Highly nonlinear–No single model to explain all
• Many simplistic models, explanations
• Many myths• Much new knowledge
Spring 2006 Utah School of Computing 4
• Many phenomena to explain
–High light / low light
–Illusions
–Color blindness
–Metamers
Spring 2006 Utah School of Computing 5
Additive Primaries: (r,g,b)Additive Primaries: (r,g,b)
(1,0,1)(1,0,1)
(0,0,1 )(0,0,1 )
(0,1,0)(0,1,0)
(0,1,1)(0,1,1)
(1,0,0)(1,0,0)
(1,1,0)(1,1,0)
magentamagentamagentamagenta
blueblueblueblue
cyancyancyancyan
greengreengreengreenredredredred
yellowyellowyellowyellow
whitewhitewhitewhite(1,1,1)(1,1,1)
Spring 2006 Utah School of Computing 6
Additive Primaries: (r,g,b)Additive Primaries: (r,g,b)
…………...<www.jgiesen.de/ColorTheory/
RGBColorApplet/rgbcolorapplet.html>
Spring 2006 Utah School of Computing 7
Traditional, Artistic:• rgb• cmy• cmyk• hsv• hsl
Perceptually Based:• XYZ (Tristimulus)• Xyz• Hunter-Lab• CIE-L*ab• CIE-L*CH° • CIE-L*CH°• CIE-L*ab• CIE-L*uv
Spring 2006 Utah School of Computing 8
Additive Primaries: (r,g,b)Additive Primaries: (r,g,b)
(0,1,0)(0,1,0)(1,0,0)(1,0,0)
(1,1,0)(1,1,0)
greengreengreengreenredredredred
yellowyellowyellowyellow
Spring 2006 Utah School of Computing 9
Additive Primaries: (r,g,b)Additive Primaries: (r,g,b)
(1,0,1)(1,0,1)
(0,0,1 )(0,0,1 )
(1,0,0)(1,0,0)
magentamagentamagentamagenta
blueblueblueblue
redredredred
Spring 2006 Utah School of Computing 10
Subtractive Primaries: (c,m,y)Subtractive Primaries: (c,m,y)
(1,1,0)(1,1,0)yellowyellowyellowyellow
(0,1,0)(0,1,0)greengreengreengreen
(0,1,1)(0,1,1)cyancyancyancyan
(0,0,1 )(0,0,1 )blueblueblueblue
(1,0,1)(1,0,1)magentamagentamagentamagenta
(1,0,0)(1,0,0)redredredred
blackblackblackblack(0,0,0)(0,0,0)
blackblackblackblack
Spring 2006 Utah School of Computing 11
Additive Primaries: (c,m,y)Additive Primaries: (c,m,y)
(0,1,0)(0,1,0)greengreengreengreen
(0,1,1)(0,1,1)cyancyancyancyan
(0,0,1 )(0,0,1 )blueblueblueblue
Spring 2006 Utah School of Computing 12
(1,1,0)(1,1,0)(1,1,0)(1,1,0)yellowyellowyellowyellow
(1,0,1)(1,0,1)(1,0,1)(1,0,1)magentamagentamagentamagenta
(1,0,0)(1,0,0)(1,0,0)(1,0,0)redredredredblackblackblackblack
Subtractive Primaries: (c,m,y)Subtractive Primaries: (c,m,y)
Spring 2006 Utah School of Computing 13
(1,1,0)(1,1,0)yellowyellowyellowyellow
(0,1,0)(0,1,0)greengreengreengreen
(0,1,1)(0,1,1)cyancyancyancyan
Subtractive Primaries: (c,m,y)Subtractive Primaries: (c,m,y)
Spring 2006 Utah School of Computing 14
(1,0,1)(1,0,1)magentamagentamagentamagenta
(0,0,1 )(0,0,1 )bbllueuebbllueue
(0,1,1)(0,1,1)cyancyancyancyan
Subtractive Primaries: (c,m,y)Subtractive Primaries: (c,m,y)
Spring 2006 Utah School of Computing 15
Wavelength Spectrum
infrared light
ultraviolet light
Wavelength (nm)700 600 500 400
• Seen in physics, physical phenomena (rainbows, prisms, etc)
• 1 Dimensional color space
Spring 2006 Utah School of Computing 16
Wavelength Spectrum
Note that the rainbow does not contain any magenta. It is nonspectral.
Spring 2006 Utah School of Computing 17
• “Navigating,” moving around in a
color space, is tricky
• Many color representations (spaces)
• Can you get to a nearby color?
• Can you predictably adjust a color?
Spring 2006 Utah School of Computing 18
Color Cube: (r,g,b) is RHSColor Cube: (r,g,b) is RHS (0,0,1 )(0,0,1 )
blueblueblueblue
(1,0,1)(1,0,1)magentamagentamagentamagenta
(0,1,1)(0,1,1)cyancyancyancyan
(0,1,0)(0,1,0)greengreengreengreen
(1,1,0)(1,1,0)yellowyellowyellowyellow
(1,0,0)(1,0,0)redredredred
whitewhitewhitewhite(1,1,1)(1,1,1)
(0,0,0)(0,0,0)blackblackblackblack
graygraygraygray
Spring 2006 Utah School of Computing 19
(0,0,1 )(0,0,1 )blueblueblueblue
(1,0,1)(1,0,1)magentamagentamagentamagenta
(0,1,1)(0,1,1)cyancyancyancyan
(0,1,0)(0,1,0)greengreengreengreen
(1,1,0)(1,1,0)yellowyellowyellowyellow(1,0,0)(1,0,0)
redredredred
(1,1,1)(1,1,1)whitewhitewhitewhite
Spring 2006 Utah School of Computing 20
Complementary Colors Add to GrayComplementary Colors Add to Gray
(0,0,1 )(0,0,1 ) (0,0,1 )(0,0,1 )blueblueblueblue
magentamagentamagentamagenta(0,1,1)(0,1,1)
cyancyancyancyan
(1,1,1)(1,1,1)whitewhitewhitewhite(1,0,1)(1,0,1)
(0,1,0)(0,1,0)(0,1,0)(0,1,0)greengreengreengreen
((1,1,01,1,0))yellowyellowyellowyellow(1,0,0)(1,0,0)
redredredred
Spring 2006 Utah School of Computing 21
Complementary ColorsComplementary Colors
Looking at color cube along major diagonal
Spring 2006 Utah School of Computing 22
James Clerk Maxwell’s ColorJames Clerk Maxwell’s Color
magentamagentamagentamagentablueblueblueblue
cyancyancyancyan
greengreengreengreen
redredredred
yellowyellowyellowyellow
unsaturated cyan unsaturated cyan unsaturated cyan unsaturated cyan
whitewhitewhitewhite
Spring 2006 Utah School of Computing 23
Newton’s Color Wheel Newton’s Color Wheel
Replaced Aristotle’s color model based on light and darkness.
Spring 2006 Utah School of Computing 24
Color AppletsColor Applets
www.cs.brown.edu/exploratories/freeSoftware/catalogs/repositoryApplets.html
Spring 2006 Utah School of Computing 25
( H, S, V ) Color Space( H, S, V ) Color Space
• Introduced by Albet Munsell, late 1800s
– He was an artist and scientist
• Hue: Color
• Saturation/Chroma: Strength of a color
– Neutral gray has 0 saturation
• Brightness/Value: Intensity of light emanating from image
Spring 2006 Utah School of Computing 26
The hue of an object may be blue,
but the terms light and dark
distinguish the brightness of one
object from another.
(Hue, Saturation, Value/Intensity)(Hue, Saturation, Value/Intensity)
( H, S, V ) Color Space
Spring 2006 Utah School of Computing 27
SaturationSaturation
Spring 2006 Utah School of Computing 28
Other HSX Color Spaces (Cones)Other HSX Color Spaces (Cones)
120˚
0˚
V
SH
1.0
0.0
240˚
yellowyellowyellowyellowgreengreengreengreen
cyancyancyancyanredredredred
magentamagentamagentamagentablueblueblueblue
blackblackblackblack
Another HSX Color Space(double cone)
Another HSX Color Space(double cone)
1.0
S
L
29
0˚
0.0
H
whitewhitewhitewhite
blackblackblackblack
redredredred
Spring 2006 Utah School of Computing 30
Tristimulus Color TheoryTristimulus Color Theory
• Any color can be matched by a mixture of three fixed base colors (primaries)
• Eye has three kinds of color receptors called cones
• Eye also has rods (low light receptors)
Spring 2006 Utah School of Computing 31
Color Receptors in EyeColor Receptors in Eye
• ((RedRed, GreenGreen, BlueBlue))
• ((LongLong, MediumMedium, ShortShort))
• ((RedRed, GreenGreen, BlueBlue))
• ((LongLong, MediumMedium, ShortShort))
Fra
ctio
n of
ligh
t ab
sorb
ed
by e
ach
type
of
cone
Wavelength λ (nm)
0.20
0 .18
0 .16
0 .14
0 .12
0 .10
0 .08
0 .06
0 .04
0 .02
0 .00
600560440 520 640400 680480
Spring 2006 Utah School of Computing 32
Color Receptors in EyeColor Receptors in Eye
600550450 500 650400 700Wavelength λ (nm)
Rel
ativ
e se
nsiti
vity
0.0
1.0
Spring 2006 Utah School of Computing 33
• Why are runway lights blue?
• Why are console lights green?
• What color is the Kodak box?
• Why are green lasers directed toward pilots for destructive purposes?
• Why do soldiers read maps in the dark using dim red light?
Color Response Color Response
Spring 2006 Utah School of Computing 34
Color Matching ExperimentsColor Matching Experiments
• Given a reference color, try to match it identically
• What does “negative red,” or “negative color” mean??
Spring 2006 Utah School of Computing 35
CIE* Color SpaceCIE* Color Space
( X, Y, Z ) represents an imaginary basis that does not correspond to what we see
Define the normalized coordinates:x = X / ( X + Y + Z )
y = Y / ( X + Y + Z )
z = Z / ( X + Y + Z )
* Commission Internationale de l'Êclairage
Spring 2006 Utah School of Computing 36
CIE Color Space of Visible ColorsCIE Color Space of Visible Colors
x + y + z = 1
x = X / ( X + Y + Z )
y = Y / ( X + Y + Z )
z = Z / ( X + Y + Z )
z
y
x
The projection of the plane of the triangle onto the (X,Y) plane forms the chromaticity diagram that follows.
Spring 2006 Utah School of Computing 37
Color Gamuts: CIE Color ChartColor Gamuts: CIE Color Charty
x
1.0
0 .9
0 .8
0 .7
0 .6
0 .5
0 .4
0 .3
0 .2
0 .1
1.0 0 .2 0 .3 0 .4 0 .5 0 .6 0 .7 0 .8 0 .9 1.0
cyancyan
magentamagentaredredblueblue
green green
yellowyellow
400
490
500
510
520
600
700
580
560
540
whitewhitewhitewhite
Spring 2006 Utah School of Computing 38
Color Gamuts: CIE Color ChartColor Gamuts: CIE Color Chart
ideal redideal redideal redideal redideal blueideal blueideal blueideal blue
ideal greenideal green ideal greenideal green
redredredredblueblueblueblue
green green green green
cyancyanyellowyellow
whitewhitewhitewhite
400 nm
490 nm
500 nm
510 nm540 nm
560 nm
580 nm
700 nm
600 nm
520 nm
Spring 2006 Utah School of Computing 39
Color Gamuts: CIE Color ChartColor Gamuts: CIE Color Chart
www.cs.rit.edu/~ncs/color/a_chroma.html
400
490
500
510
520
600
700
580
560
540
Spring 2006 Utah School of Computing 40
Color Gamuts: CIE Color ChartColor Gamuts: CIE Color Chart
www.cs.rit.edu/~ncs/color/a_chroma.html
700 nm
510 nm
400 nm
500 nm
490 nm
600 nm
520 nm540 nm
560 nm
580 nm
Spring 2006 Utah School of Computing 41
Color Gamuts: CIE Color ChartColor Gamuts: CIE Color Chart
www.cs.rit.edu/~ncs/color/a_chroma.html
400
490
500
510
520
600
700
580
560
540
C2
C3
C1
The additive colors C1and
C2 combine to
form C3 on the
line connecting C1 and C2.
Spring 2006 Utah School of Computing 42
Color Gamuts: CIE Color ChartColor Gamuts: CIE Color Chart
www.cs.rit.edu/~ncs/color/a_chroma.html
400
490
500
510
520
600
700
580
560
540
R
B
G
The Color Gamuts of different displays and printers are not likely to match. Printers usually have smaller gamuts.
Spring 2006 Utah School of Computing 43
www.cs.brown.edu/exploratories/freeSoftware/repository/edu/brown/cs/exploratories/applets/
combinedColorMixing/combined_color_mixing_java_browser.html
Spring 2006 Utah School of Computing 44
CIE L*a*b* Color SpaceCIE L*a*b* Color Space
greengreengreengreen
--bb**
L*=0
L*=1
light
ness
redredredred
blueblue
blueblue
++aa**++aa**
-a*-a*-a*-a*
yello
wye
llow
yello
wye
llow
+b*+b*+b*+b*
Equal distances represent
approximately equal color difference.
whitewhitewhitewhite
blackblackblackblack
Spring 2006 Utah School of Computing 45
Important ConceptsImportant Concepts
• Adaptation–Slow process
• Constancy–Immediate process
Spring 2006 Utah School of Computing 46
Output to the Brain from Lateral Geniculate BodyOutput to the Brain from Lateral Geniculate Body
RRRR
GGGG
BBBB
+
+
-
+
-
B B - B B -
RR- GGRR- GG
BBBB
RRRR
GGGG
YYYY
YYYY
+
Color processing unit: lateral geniculate body
Spring 2006 Utah School of Computing 47
Eye’s MechanismEye’s Mechanism
+++ +++
+++
- --
+++
- --
+++
- --
+++
- --
- --
- --
- --
- --
- --
- --
- --
- --- -
-
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48
Lect
ure
Set
11