why is this hard to read
DESCRIPTION
Why is this hard to read. Unrelated vs. Related Color. Unrelated color: color perceived to belong to an area in isolation (CIE 17.4) Related color: color perceived to belong to an area seen in relation to other colors (CIE 17.4). Illusory contour. Shape, as well as color, depends on surround - PowerPoint PPT PresentationTRANSCRIPT
Why is this hard to read
Unrelated vs. Related Color
• Unrelated color: color perceived to belong to an area in isolation (CIE 17.4)
• Related color: color perceived to belong to an area seen in relation to other colors (CIE 17.4)
Illusory contour
• Shape, as well as color, depends on surround
• Most neural processing is about differences
Illusory contour
CS 768 Color Science
• Perceiving color
• Describing color
• Modeling color
• Measuring color
• Reproducing color
Spectral measurement
• Measurement p() of the power (or energy, which is power x time ) of a light source as a function of wavelength
• Usually relative to p(560nm)
• Visible light 380-780 nm
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Spectral Distribution of daylight
Rel
ati
ve P
ow
er
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Normalized to 560 nm
leaf
flower
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ati
ve P
ow
er
Spectra of a red flower and a green leaf
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Normalized to spectrometer max
leaf
flower
Abs
olu
te P
ow
er
Data from http://www.it.lut.fi/research/color/database/database.html
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Retinal line spread function
retinal position
rela
tive
inte
nsit
y
Linearity
• additivity of response (superposition)
• r(m1+m2)=r(m1)+r(m2)
• scaling (homogeneity)• r(m)=r(m)• r(m1(x,y)+m2 (x,y))=
r(m1)(x,y)+r(m2)(x,y)= (r(m1)+r(m2))(x,y)
• r(m(x,y))=r(m)(x,y)
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2Superposition
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reti
nal i
nten
sity
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itor
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nsit
y
Non-linearity
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stimulus
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ons
e
adaptive architecture provides more sensitivity over smaller range
http://webvision.med.utah.edu/
Ganglion
Bipolar
Amacrine
Rod Cone
Epithelium
Optic nerve
Retinal cross section
Light
Horizontal
Visual pathways
• Three major stages– Retina
– LGN
– Visual cortex
– Visual cortex is further subdivided
http://webvision.med.utah.edu/Color.html
Optic nerve
• 130 million photoreceptors feed 1 million ganglion cells whose output is the optic nerve.
• Optic nerve feeds the Lateral Geniculate Nucleus approximately 1-1
• LGN feeds area V1 of visual cortex in complex ways.
Photoreceptors
• Cones - – respond in high (photopic) light– differing wavelength responses (3 types)– single cones feed retinal ganglion cells so give
high spatial resolution but low sensitivity– highest sampling rate at fovea
Photoreceptors
• Rods– respond in low (scotopic) light– none in fovea
• try to foveate a dim star—it will disappear
– one type of spectral response– several hundred feed each ganglion cell so give
high sensitivity but low spatial resolution
Luminance
• Light intensity per unit area at the eye
• Measured in candelas/m2 (in cd/m2)
• Typical ambient luminance levels (in cd/m2): – starlight 10-3
– moonlight 10-1
– indoor lighting 102
– sunlight 105
– max intensity of common CRT monitors 10^2 From Wandell, Useful Numbers in Vision Science
http://white.stanford.edu/~brian/numbers/numbers.html
Rods and cones
• Rods saturate at 100 cd/m2 so only cones work at high (photopic) light levels
• All rods have the same spectral sensitivity
• Low light condition is called scotopic
• Three cone types differ in spectral sensitivity and somewhat in spatial distribution.
Cones
• L (long wave), M (medium), S (short)– describes sensitivity curves.
• “Red”, “Green”, “Blue” is a misnomer. See spectral sensitivity.
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wavelength (nm)
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ne r
esp
ons
e
Cone Spectral Responses
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wavelength (nm)
Con
e r
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Cone Spectral Responses
Receptive fields
• Each neuron in the visual pathway sees a specific part of visual space, called its receptive field
• Retinal and LGN rf’s are circular, with opponency; Cortical are oriented and sometimes shape specific.
--
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--
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On center rf Red-Green LGN rf
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Oriented Cortical rf
Channels: Visual Pathways subdivided
• Channels• Magno
– Color-blind
– Fast time response
– High contrast sensitivity
– Low spatial resolution
• Parvo
– Color selective
– Slow time response
– Low contrast sensitivity
– High spatial resolution
• Video coding implications• Magno
– Separate color from b&w
– Need fast contrast changes (60Hz)
– Keep fine shading in big areas
– (Definition)
• Parvo
– Separate color from b&w
– Slow color changes OK (40 hz)
– Omit fine shading in small areas
– (Definition)
• (Not obvious yet) pattern detail can be all in b&w channel
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V'() V()
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wavelength (nm)
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e r
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Trichromacy
• Helmholtz thought three separate images went forward, R, G, B.
• Wrong because retinal processing combines them in opponent channels.
• Hering proposed opponent models, close to right.
Opponent Models
• Three channels leave the retina:– Red-Green (L-M+S = L-(M-S))– Yellow-Blue(L+M-S)– Achromatic (L+M+S)
• Note that chromatic channels can have negative response (inhibition). This is difficult to model with light.
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wavelength (nm)
RedGreen L-M+S BlueYellow L+M-S
Achromatic L+M+.05*S
Schematic color opponent response
+- +
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V()
wavelength (nm)
RedGreen L-M+S BlueYellow L+M-S
Achromatic L+M+.05*S
Schematic color opponent response
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Log Spatial Frequency (cpd)
Con
tras
t Sen
siti
vity
Luminance
Red-Green
Blue-Yellow
Color matching
• Grassman laws of linearity:()(((
• Hence for any stimulus s() and response
r(), total response is integral of s() r(), taken over all or approximately s()r()
Primarylights
Test light
Bipartitewhitescreen
Surround field
Test light Primary lights
Subject
Surround light
Color Matching
• Spectra of primary lights s1(), s2(), s3()
• Subject’s task: find c1, c2, c3, such thatc1s1()+c2s2()+c3s3()
matches test light.
• Problems (depending on si())
– [c1,c2,c3] is not unique (“metamer”)
– may require some ci<0 (“negative power”)
Color Matching
• Suppose three monochromatic primaries r,g,b at 645.16, 526.32, 444.44 nm and a 10° field (Styles and Burch 1959).
• For any monochromatic light t() at find scalars R=R(G=G(B=B(such that
t() = R(rG(gB(b• R(,G(,B(are the color matching
functions based on r,g,b.
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3.5Stiles and Burch 1959 10-degree bipartite field color matching functions
primary lights at 645.2 nm 525.3 nmand 444.4 nm
b10() g10()
r10()
Color matching
• Grassman laws of linearity:()(((
• Hence for any stimulus s() and response
r(), total response is integral of s() r(), taken over all or approximately s()r()
Color matching
• What about three monochromatic lights?• M() = R*R() + G*G() + B*B()• Metamers possible• good: RGB functions are like cone
response• bad: Can’t match all visible lights with any
triple of monochromatic lights. Need to add some of primaries to the matched light
Primarylights
Test light
Bipartitewhitescreen
Surround field
Test light Primary lights
Subject
Surround light
Color matching
• Solution: CIE XYZ basis functions
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z
y x
CIE 1931 standard colorimetric observer color matching functions
Tris
timul
us v
alue
wavelength (nm)
Color matching
• Note Y is V()
• None of these are lights
• Euclidean distance in RGB and in XYZ is not perceptually useful.
• Nothing about color appearance
XYZ problems
• No correlation to perceptual chromatic differences
• X-Z not related to color names or daylight spectral colors
• One solution: chromaticity
Chromaticity Diagrams
• x=X/(X+Y+Z)y=Y/(X+Y+Z)z=Z/(X+Y+Z)
• Perspective projection on X-Y plane
• z=1-(x-y), so really 2-d
• Can recover X,Y,Z given x,y and on XYZ, usually Y since it is luminance
Chromaticity Diagrams
• No color appearance info since no luminance info.
• No accounting for chromatic adaptation.
• Widely misused, including for color gamuts.
Some gamuts
SWOP
ENCAD GA ink
MacAdam Ellipses
• JND of chromaticity
• Bipartite equiluminant color matching to a given stimulus.
• Depends on chromaticity both in magnitude and direction.
MacAdam Ellipses
• For each observer, high correlation to variance of repeated color matches in direction, shape and size– 2-d normal distributions are ellipses– neural noise?
• See Wysecki and Styles, Fig 1(5.4.1) p. 307
MacAdam Ellipses
• JND of chromaticity – Weak inter-observer correlation in size, shape,
orientation. • No explanation in Wysecki and Stiles 1982
• More modern models that can normalize to observer?
MacAdam Ellipses
• JND of chromaticity – Extension to varying luminence: ellipsoids in
XYZ space which project appropriately for fixed luminence
MacAdam Ellipses
• JND of chromaticity – Technology applications:
• Bit stealing: points inside chromatic JND ellipsoid are not distinguishable chromatically but may be above luminance JND. Using those points in RGB space can thus increase the luminance resolution. In turn, this has appearance of increased spatial resolution (“anti-aliasing”)
• Microsoft ClearType. See http://www.grc.com/freeandclear.htm and http://www.ductus.com/cleartype/cleartype.html
Complementary Colors
• Colors which sum to white point are called complementary colors
• a*c1+b*c2 = wp• Some monochromatic colors have
complements, others don’t. See ComplementaryColors.m
• Complements may be out of gamut. See Photoshop.