Transcript CS 445 / 645: Introductory Computer Graphics Color Midterm Exam The Midterm Exam will be Tuesday, October 23rd Review Session will be Thursday, October 18th.

CS 445 / 645:
Introductory Computer
Graphics
Color
Midterm Exam
The Midterm Exam will be Tuesday,
October 23rd
Review Session will be Thursday,
October 18th.
Assignment 3
• Assignment 3 goes out today
• Due Sunday, October 21st at Midnight
Color
• Next topic: Color
To understand how to make realistic
images, we need a basic understanding of
the physics and physiology of vision. Here
we step away from the code and math for a
bit to talk about basic principles.
Basics Of Color
• Elements of color:
Basics of Color
• Physics:
– Illumination
• Electromagnetic spectra
– Reflection
• Material properties
• Surface geometry and microgeometry (i.e., polished
versus matte versus brushed)
• Perception
– Physiology and neurophysiology
– Perceptual psychology
Physiology of Vision
• The eye:
• The retina
– Rods
– Cones
• Color!
Physiology of Vision
• The center of the retina is a densely
packed region called the fovea.
– Cones much denser here than the
periphery
Physiology of Vision: Cones
• Three types of cones:
– L or R, most sensitive to red light (610 nm)
– M or G, most sensitive to green light (560 nm)
– S or B, most sensitive to blue light (430 nm)
– Color blindness results from missing cone type(s)
Physiology of Vision: The
Retina
• Strangely, rods and
cones are at the
back of the retina,
behind a mostlytransparent neural
structure that
collects their
response.
• http://www.trueorigin.o
rg/retina.asp
Perception: Metamers
• A given perceptual sensation of color
derives from the stimulus of all three
cone types
• Identical
perceptions
of color can
thus be
caused by
very different spectra
Perception: Other Gotchas
• Color perception is also difficult because:
– It varies from person to person
– It is affected by adaptation (stare at a light
bulb… don’t)
– It is affected by surrounding color:
Perception: Relative Intensity
We are not good at judging absolute intensity
• Let’s illuminate pixels with white light on scale
of 0 - 1.0
• Intensity difference of neighboring colored
rectangles with intensities:
– 0.10 -> 0.11 (10% change)
– 0.50 -> 0.55 (10% change)
will look the same
• We perceive relative intensities, not absolute
Representing Intensities
• Remaining in the world of black and white…
• Use photometer to obtain min and max
brightness of monitor
• This is the dynamic range
• Intensity ranges from min, I0, to max, 1.0
• How do we represent 256 shades of gray?
Representing Intensities
• Equal distribution between min and max fails
– relative change near max is much smaller
than near I0
I0=I0
– Ex: ¼, ½, ¾, 1
I1 = rI0
• Preserve % change
– Ex: 1/8, ¼, ½, 1
– In = I0 * rnI0, n > 0
I2 = rI1 = r2I0
…
I255=rI254=r255I0
Dynamic Ranges
•
•
•
•
•
•
Dynamic Range Max # of
Display
(max / min illum) Perceived
Intensities (r=1.01)
CRT:
50-200
400-530
Photo (print) 100
465
Photo (slide) 1000
700
B/W printout 100
465
Color printout 50
400
Newspaper
10
234
Gamma Correction
• But most display devices are inherently
nonlinear: Intensity = k(voltage)g
– i.e., brightness(voltage) != 2*brightness(voltage/2)
 g is between 2.2 and 2.5 on most monitors
• Common solution: gamma correction
– Post-transformation on intensities to map them to
linear range on display device:
1
– Can have separate g for R, G, B
g
yx
Gamma Correction
• Some monitors perform the gamma
correction in hardware (SGI’s)
• Others do not (most PCs)
• Tough to generate images that look
good on both platforms (i.e. images
from web pages)
Halftoning
• A technique used in newspaper printing
• Only two intensities are possible, blob of ink and
no blob of ink
• But, the size of the blob can be varied
• Also, the dither patterns of small dots can be used
Halftoning
Halftoning
Back to color
• Color is defined many ways
• Computer scientists frequently use:
– Hue - The color we see (red, green, purple)
– Saturation - How far is the color from gray (pink
is less saturated than red, sky blue is less
saturated than royal blue)
– Brightness (Luminance) - How bright is the color
How well do we see color?
• What color do we see the best?
– Yellow-green at 550 nm
• What color do we see the worst?
– Blue at 440 nm
• Flashback: Colortables (colormaps) for color storage
• Can perceive color differences of 10 nm at
extremes (violet and red) and 2 nm between
blue and yellow
How well do we see color?
• 128 fully saturated hues can be
distinguished
• Cannot perceive hue differences with
less saturated light.
• Sensitivity to changes in saturation for a
fixed hue and brightness ranges from
16 to 23 depending on hue.
Combining Colors
Additive (RGB)
Subtractive (CMYK)
Color Spaces
• Three types of cones suggests color is a 3D
quantity. How to define 3D color space?
• Idea: shine given wavelength () on a screen,
and mix three other wavelengths (R,G,B) on
same screen. Have user adjust intensity of
RGB until colors are identical:
CIE Color Space
• The CIE (Commission Internationale
d’Eclairage) came up with three hypothetical
lights X, Y, and Z with these spectra:
Note that:
X~R
Y~G
Z~B
• Idea: any wavelength  can be matched
perceptually by positive combinations of X,Y,Z
CIE Color Space
• The gamut of all colors perceivable is thus a
three-dimensional shape in X,Y,Z
• Color = X’X + Y’Y + Z’Z
CIE Chromaticity Diagram
(1931)
For simplicity, we
often project to the
2D plane X’+Y’+Z’=1
X’ = X’ / (X’+Y’+Z’)
Y’ = Y’ / (X’+Y’+Z’)
Z’ = 1 – X’ – Y’
Device Color Gamuts
• Since X, Y, and Z are hypothetical light
sources, no real device can produce the
entire gamut of perceivable color
• Example: CRT monitor
RGB Color Space
• Define colors with (r, g, b) amounts of
red, green, and blue
Device Color Gamuts
• The RGB color cube sits within CIE
color space something like this:
Device Color Gamuts
• We can use the CIE chromaticity
diagram to compare the gamuts of
various devices:
• Note, for example,
that a color printer
cannot reproduce
all shades available
on a color monitor
Converting Color Spaces
• Simple matrix operation:
 R '   XR
G '   YR
  
 B '  ZR
XG
YG
ZG
XB   R 
YB  G 
ZB   B 
• The transformation C2 = M-12 M1 C1
yields RGB on monitor 2 that is
equivalent to a given RGB on monitor 1
Converting Color Spaces
• Converting between color models can also
be expressed as such a matrix transform:
0.11   R 
Y  0.30 0.59
 I   0.60  0.28  0.32 G 
  
 
Q   0.21  0.52 0.31   B 
• Note the relative unimportance of blue in
computing the Y
• YIQ is the color model used for color TV in
America. Y is luminance, I & Q are color
– Note: Y is the same as CIE’s Y
– Result: backwards compatibility with B/W TV!
HSV Color Space
• A more intuitive color space
– H = Hue
– S = Saturation
– V = Value (or brightness)
Hue
Saturation
Value