CS5600 Computer Graphics by Rich Riesenfeld Spring 2006 Lecture Set 11

Spring 2006Utah School of Computing2 Physical nature of color Eye mechanism of color –Rods, cones, tri-stimulus model Brain mechanism of color Color spaces Aesthetic and physiological

Spring 2006Utah School of Computing3 Color is complicated! –Highly nonlinear –No single model to explain all Many simplistic models, explanations Many myths Much new knowledge

Spring 2006Utah School of Computing4 Many phenomena to explain –High light / low light –Illusions –Color blindness –Metamers

Spring 2006Utah School of Computing5 Additive Primaries: ( r,g,b ) (1,0,1) (0,0,1 ) (0,1,0) (0,1,1) (1,0,0) (1,1,0) magentamagenta blueblue cyancyan greengreen redred yellowyellow whitewhite (1,1,1)

Spring 2006Utah School of Computing6 Additive Primaries: (r,g,b) …………... ColorTheory/RGBColorApplet /rgbcolorapplet.html

Spring 2006Utah School of Computing7 Traditional, Artistic: rgb cmy cmyk hsv hsl Perceptually Based: XYZ (Tristimulus) Xyz Hunter-Lab CIE-L*ab CIE-L*CH° CIE-L*ab CIE-L*uv

Spring 2006Utah School of Computing8 Additive Primaries: ( r,g,b ) (0,1,0) (1,0,0) (1,1,0) greengreen redred yellowyellow

Spring 2006Utah School of Computing9 Additive Primaries: (r,g,b) (1,0,1) (0,0,1 ) (1,0,0) magentamagenta blueblue redred

Spring 2006Utah School of Computing10 Subtractive Primaries: ( c, m, y ) (1,1,0) yellowyellow (0,1,0) greengreen (0,1,1) cyancyan (0,0,1 ) blueblue (1,0,1) magentamagenta (1,0,0)redred blackblack (0,0,0) blackblack

Spring 2006Utah School of Computing11 Additive Primaries: ( c, m, y ) (0,1,0) greengreen (0,1,1) cyancyan (0,0,1 ) blueblue

Spring 2006Utah School of Computing12 (1,1,0)(1,1,0) yellowyellow (1,0,1)(1,0,1) magentamagenta (1,0,0)(1,0,0) redred blackblack Subtractive Primaries: (c,m,y)

Spring 2006Utah School of Computing13 (1,1,0) yellowyellow (0,1,0) greengreen (0,1,1) cyancyan Subtractive Primaries: (c,m,y)

Spring 2006Utah School of Computing14 (1,0,1) magentamagenta (0,0,1 ) b l ue (0,1,1) cyancyan Subtractive Primaries: (c,m,y)

Spring 2006Utah School of Computing15 Wavelength Spectrum infrared light ultraviolet light Wavelength (nm) Seen in physics, physical phenomena (rainbows, prisms, etc) 1 Dimensional color space

Spring 2006Utah School of Computing16 Wavelength Spectrum Note that the rainbow does not contain any magenta. It is nonspectral.

Spring 2006Utah School of Computing17 “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 2006Utah School of Computing18 Color Cube: ( r,g,b ) is RHS (0,0,1 ) blueblue (1,0,1) magentamagenta (0,1,1) cyancyan (0,1,0) greengreen (1,1,0) yellowyellow (1,0,0) redred whitewhite (1,1,1) (0,0,0) blackblackgraygray

Spring 2006Utah School of Computing19 (0,0,1 ) blueblue (1,0,1) magentamagenta (0,1,1) cyancyan (0,1,0) greengreen (1,1,0) yellowyellow (1,0,0) redred (1,1,1) whitewhite

Spring 2006Utah School of Computing20 Complementary Colors Add to Gray (0,0,1 ) blueblue magentamagenta (0,1,1) cyancyan (1,1,1) whitewhite (1,0,1) (0,1,0)(0,1,0) greengreen ( 1,1,0 ) yellowyellow (1,0,0) redred

Spring 2006Utah School of Computing21 Complementary Colors Looking at color cube along major diagonal

Spring 2006Utah School of Computing22 James Clerk Maxwell’s Color  magentamagenta blueblue cyancyan greengreen redred yellowyellow unsaturated cyan whitewhite

Spring 2006Utah School of Computing23 Newton’s Color Wheel Replaced Aristotle’s color model based on light and darkness.

Spring 2006Utah School of Computing24 Color Applets /catalogs/repositoryApplets.html

Spring 2006Utah School of Computing25 ( 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 2006Utah School of Computing26 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 ) ( H, S, V ) Color Space

Spring 2006Utah School of Computing27 Saturation

Spring 2006Utah School of Computing28 Other HSX Color Spaces ( Cones ) 120 ˚ 0˚0˚ V S H ˚ yellowyellow greengreen cyancyan redred magentamagentablueblue blackblack

Another HSX Color Space ( double cone ) 1.0 S L 29 0˚0˚ 0.0 H whitewhite blackblack redred

Spring 2006Utah School of Computing30 Tristimulus 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 2006Utah School of Computing31 Color Receptors in Eye (Red Green Blue) (Red, Green, Blue) (Long Medium Short) (Long, Medium, Short) (Red Green Blue) (Red, Green, Blue) (Long Medium Short) (Long, Medium, Short) Fraction of light absorbed by each type of cone Wavelength λ ( nm )

Spring 2006Utah School of Computing32 Color Receptors in Eye Wavelength λ ( nm ) Relative sensitivity

Spring 2006Utah School of Computing33 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

Spring 2006Utah School of Computing34 Color Matching Experiments Given a reference color, try to match it identically What does “negative red,” or “negative color” mean??

Spring 2006Utah School of Computing35 CIE* 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 2006Utah School of Computing36 CIE 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 2006Utah School of Computing37 Color Gamuts: CIE Color Chart cyan magenta red blue green yellow whitewhite

Spring 2006Utah School of Computing38 Color Gamuts: CIE Color Chart ideal red ideal blue ideal green redred blueblue greengreen cyan yellow whitewhite 400 nm 490 nm 500 nm 510 nm 540 nm 560 nm 580 nm 700 nm 600 nm 520 nm

Spring 2006Utah School of Computing39 Color Gamuts: CIE Color Chart

Spring 2006Utah School of Computing40 Color Gamuts: CIE Color Chart nm 510 nm 400 nm 500 nm 490 nm 600 nm 520 nm 540 nm 560 nm 580 nm

Spring 2006Utah School of Computing41 Color Gamuts: CIE Color Chart C2C2 C3C3 C1C1 The additive colors C 1 and C 2 combine to form C 3 on the line connecting C 1 and C 2.

Spring 2006Utah School of Computing42 Color Gamuts: CIE Color Chart R B G The Color Gamuts of different displays and printers are not likely to match. Printers usually have smaller gamuts.

Spring 2006Utah School of Computing43 edu/brown/cs/exploratories/applets/combinedColorMixing /combined_color_mixing_java_browser.html

Spring 2006Utah School of Computing44 CIE L*a*b* Color Space greengreen -b*-b*-b*-b* L*=0 L*=1 lightness redred blueblue +a*+a*+a*+a* +a*+a*+a*+a* -a*-a* yellowyellow +b*+b* Equal distances represent approximately equal color difference. whitewhite blackblack

Spring 2006Utah School of Computing45 Important Concepts Adaptation –Slow process Constancy –Immediate process

Spring 2006Utah School of Computing46 Output to the Brain from Lateral Geniculate Body RR GG BB B B - RG R - G BB RR GG YY YY + Color processing unit: lateral geniculate body

Spring 2006Utah School of Computing47 Eye’s Mechanism

48 Lecture Set 11