light and color jehee lee seoul national university with a lot of slides stolen from alexei efros,...
TRANSCRIPT
Light and Color
Jehee Lee
Seoul National University
With a lot of slides stolen from Alexei Efros, Stephen Palmer, Fredo Durand and others
The Eye
• The human eye is a camera!– Iris - colored annulus with radial muscles
– Pupil - the hole (aperture) whose size is controlled by the iris– What’s the “film”?
• photoreceptor cells (rods and cones) in the retina
The Retina
Cross-section of eye
Ganglion cell layer
Bipolar cell layer
Receptor layer
Pigmentedepithelium
Ganglion axons
Cross section of retina
© Stephen E. Palmer, 2002
Cones cone-shaped less sensitive operate in high light color vision
Two types of light-sensitive receptors
cone
rod
Rods rod-shaped highly sensitive operate at night gray-scale vision
© Stephen E. Palmer, 2002
Distribution of Rods and Cones
.
0
150,000
100,000
50,000
020 40 60 8020406080
Visual Angle (degrees from fovea)
Rods
Cones Cones
Rods
FoveaBlindSpot
# R
ecep
tors
/mm
2
Night Sky: why are there more stars off-center?
Why do we see light of these wavelengths?
© Stephen E. Palmer, 2002
.
0 1000 2000 3000
En
erg
y
Wavelength (nm)
400 700
700 C
2000 C
5000 C
10000 C
VisibleRegion
…because that’s where theSun radiates EM energy
Visible Light
The Physics of Light
Any patch of light can be completely describedphysically by its spectrum: the number of photons (per time unit) at each wavelength 400 - 700 nm.
400 500 600 700
Wavelength (nm.)
# Photons(per ms.)
© Stephen E. Palmer, 2002
The Physics of Light
.
# P
ho
ton
s
D. Normal Daylight
Wavelength (nm.)
B. Gallium Phosphide Crystal
400 500 600 700
# P
ho
ton
s
Wavelength (nm.)
A. Ruby Laser
400 500 600 700
400 500 600 700
# P
ho
ton
s
C. Tungsten Lightbulb
400 500 600 700
# P
ho
ton
s
Some examples of the spectra of light sources
© Stephen E. Palmer, 2002
Horn, 1986
),,(
),,(),,,,(
ii
eeeeii E
LfBRDF
,, ii
,, ee
Radiometry for color
Spectral radiance: power in a specified direction, per unit area, per unit solid angle, per unit wavelength
Spectral irradiance: incident power per unit area, per unit wavelength
Simplified rendering models: reflectance
Often are more interested in relative spectral composition than in overall intensity, so the spectral BRDF computation simplifies a wavelength-by-wavelength multiplication of relative energies.
.* =
Foundations of Vision, by Brian Wandell, Sinauer Assoc., 1995
.* =
Simplified rendering models: transmittance
Foundations of Vision, by Brian Wandell, Sinauer Assoc., 1995
The Physics of Light
Some examples of the reflectance spectra of surfaces
Wavelength (nm)
% P
hoto
ns R
efle
cted
Red
400 700
Yellow
400 700
Blue
400 700
Purple
400 700
© Stephen E. Palmer, 2002
The Psychophysical Correspondence
There is no simple functional description for the perceivedcolor of all lights under all viewing conditions, but …...
A helpful constraint: Consider only physical spectra with normal distributions
area
Wavelength (nm.)
# Photons
400 700500 600
mean
variance
© Stephen E. Palmer, 2002
The Psychophysical Correspondence
Mean Hue
yellowgreenblue
# P
hoto
ns
Wavelength
© Stephen E. Palmer, 2002
The Psychophysical Correspondence
Variance Saturation
Wavelength
high
medium
low
hi.
med.
low# P
hoto
ns
© Stephen E. Palmer, 2002
The Psychophysical Correspondence
Area Brightness#
Pho
tons
Wavelength
B. Area Lightness
bright
dark
© Stephen E. Palmer, 2002
© Stephen E. Palmer, 2002
.
400 450 500 550 600 650
RE
LAT
IVE
AB
SO
RB
AN
CE
(%
)
WAVELENGTH (nm.)
100
50
440
S
530 560 nm.
M L
Three kinds of cones:
Physiology of Color Vision
Color matching experiment 2
p1 p2 p3 p1 p2 p3
We say a “negative” amount of p2 was needed to make the match, because we added it to the test color’s side.
The primary color amounts needed for a match:
p1 p2 p3
Grassman’s Laws
• For color matches:– symmetry: U=V <=>V=U– transitivity: U=V and V=W => U=W– proportionality: U=V <=> tU=tV– additivity: if any two (or more) of the statements
U=V, W=X, (U+W)=(V+X) are true, then so is the third
• These statements are as true as any biological law. They mean that additive color matching is linear.
Forsyth & Ponce
Since we can define colors using almost any set of primary colors, let’s agree on a set of primaries and color matching functions for the world to use…
CIE XYZ color space
• Commission Internationale d’Eclairage, 1931• “…as with any standards decision, there are some
irratating aspects of the XYZ color-matching functions as well…no set of physically realizable primary lights that by direct measurement will yield the color matching functions.”
• “Although they have served quite well as a technical standard, and are understood by the mandarins of vision science, they have served quite poorly as tools for explaining the discipline to new students and colleagues outside the field.”
Foundations of Vision, by Brian Wandell, Sinauer Assoc., 1995
CIE XYZ: Color matching functions are positive everywhere, but primaries are “imaginary” (require adding light to the test color’s side in a color matching experiment). Usually compute x, y, where x=X/(X+Y+Z)
y=Y/(X+Y+Z)Foundations of Vision, by Brian Wandell, Sinauer Assoc., 1995
A qualitative rendering of the CIE (x,y) space. The blobby region represents visible colors. There are sets of (x, y) coordinates that don’t represent real colors, because the primaries are not real lights (so that the color matching functions could be positive everywhere).
Forsyth & Ponce
• CIE chromaticity diagram encompasses all the perceivable colors in 2D space (x,y) by ignoring the luminance
A plot of the CIE (x,y) space. We show the spectral locus (the colors of monochromatic lights) and the black-body locus (the colors of heated black-bodies). I have also plotted the range of typical incandescent lighting.
Forsyth & Ponce
Color Gamut
• The color gamut for n primaries in CIE chromaticity diagram is the convexhull of the color positions
Dominant Wavelength
• The spectral color which can be mixed with white light in order to reproduce the desired color
• C2 have spectral distributions with subtractive dominant wave lengths
CIE color space
• Can think of X, Y , Z as coordinates
• Linear transform from typical RGB or LMS
• Always positive(because physical spectrum is positive and matching curves are positives)
• Note that many points in XYZ do not correspond to visible colors!
XYZ vs. RGB
• Linear transform• XYZ is rarely used for storage• There are tons of flavors of RGB
– sRGB, Adobe RGB– Different matrices!
• XYZ is more standardized• XYZ can reproduce all colors with positive values• XYZ is not realizable physically !!
– What happens if you go “off” the diagram– In fact, the orthogonal (synthesis) basis of XYZ requires
negative values.
RGB color space
• RGB cube– Easy for devices– But not perceptual– Where do the grays live?– Where is hue and saturation?
HSV
• Hue, Saturation, Value (Intensity)– RGB cube on its vertex
• Decouples the three components (a bit)• Use rgb2hsv() and hsv2rgb() in Matlab
Color names for cartoon spectra
400 500 600 700 nm
400 500 600 700 nm
400 500 600 700 nm
red
gree
nbl
ue
400 500 600 700 nm
cyan
mag
enta
yell
ow
400 500 600 700 nm
400 500 600 700 nm
Additive color mixing
400 500 600 700 nm
400 500 600 700 nm
red
gree
n
Red and green make…
400 500 600 700 nm
yell
ow
Yellow!
When colors combine by adding the color spectra. Example color displays that follow this mixing rule: CRT phosphors, multiple projectors aimed at a screen, Polachrome slide film.
Simplified rendering models: reflectance
Often are more interested in relative spectral composition than in overall intensity, so the spectral BRDF computation simplifies a wavelength-by-wavelength multiplication of relative energies.
.* =
Foundations of Vision, by Brian Wandell, Sinauer Assoc., 1995
Subtractive color mixing
When colors combine by multiplying the color spectra. Examples that follow this mixing rule: most photographic films, paint, cascaded optical filters, crayons.
400 500 600 700 nm
cyan
yell
ow
400 500 600 700 nm
Cyan and yellow (in crayons,called “blue” and yellow) make…
400 500 600 700 nmGreen!gr
een
subtractive model (colors of pigments are subtracted) used in color output devices
CMYK color model - K for black ink for reducing the amount of ink
CMY color model
Uniform color spaces
• McAdam ellipses (next slide) demonstrate that differences in x,y are a poor guide to differences in color
• Construct color spaces so that differences in coordinates are a good guide to differences in color.
Forsyth & Ponce
Variations in color matches on a CIE x, y space. At the center of the ellipse is the color of a test light; the size of the ellipse represents the scatter of lights that the human observers tested would match to the test color; the boundary shows where the just noticeable difference is. The ellipses on the left have been magnified 10x for clarity; on the right they are plotted to scale. The ellipses are known as MacAdam ellipses after their inventor. The ellipses at the top are larger than those at the bottom of the figure, and that they rotate as they move up. This means that the magnitude of the difference in x, y coordinates is a poor guide to the difference in color.
Forsyth & Ponce
Perceptually Uniform Space: MacAdam
• In perceptually uniform color space, Euclidean distances reflect perceived differences between colors
• MacAdam ellipses (areas of unperceivable differences) become circles
• Non-linear mapping, many solutions have been proposed
Source: [Wyszecki and Stiles ’82]
CIELAB (a.k.a. CIE L*a*b*)
Source: [Wyszecki and Stiles ’82]
• The reference perceptually uniform color space
• L: lightness • a and b: color opponents
• X0, Y0, and Z0 are used to color-balance: they’re the color of the reference white
White Balance
• Chromatic adaptation– If the light source is gradually changed in color,
humans will adapt and still perceive the color of the surface the same