light and color - cs.princeton.edu · tristimulus color •any distribution of light can be...
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Light and Color
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Problems
• How do cameras measure light and color? – Radiometry
• How do humans perceive light and color? – Photometry
• How do computers represent light and color?
• How do monitors display light and color?
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Intensity
• Perception of intensity is nonlinear
Amount of light
Perceived brightness
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Modeling Nonlinear Intensity Response
• Brightness (B) usually modeled as a logarithm or power law of intensity (I)
• Exact curve varies with ambient light, adaptation of eye
3/1
logIB
IkB=
=
I
B
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Adelson’s Checker Illusion
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Adelson’s Checker Illusion
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CRT Response
• Power law for Intensity (I) vs. applied voltage (V)
• Other displays (e.g. LCDs) contain electronics to emulate this law
5.2==
γ
γVI
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Digression: Monitor Knobs
• “Brightness” knob is offset
• “Contrast” knob is scale
• Yes, the names are misleading…
γ)( brightnessVcontrastI +⋅=
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Cameras
• Original cameras based on Vidicon obey power law for Voltage (V) vs. Intensity (I):
• Vidicon + CRT = almost linear!
45.0==
γ
γIV
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CCD Cameras
• Camera gamma codified in NTSC standard
• CCDs have linear response to incident light
• Electronics to apply required power law
• So, pictures from most cameras (including digital still cameras) will have γ = 0.45
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Consequences for Vision
• Output of most cameras is not linear
• Know what it is! (Sometimes system automagically applies “gamma correction”)
• Necessary to correct raw pixel values for: – Reflectance measurements
– Shape from shading
– Photometric stereo
– Recognition under variable lighting
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Consequences for Vision
• What about e.g. edge detection? – Often want “perceptually significant” edges
– Standard nonlinear signal close to (inverse of) human response
– Using nonlinear signal often the “right thing”
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Contrast Sensitivity
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Contrast Sensitivity
• Contrast sensitivity for humans about 1%
• 8-bit image (barely) adequate if using perceptual (nonlinear) mapping
• Frequency dependent: contrast sensitivity lower for high and very low frequencies
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Contrast Sensitivity
• Campbell-Robson contrast sensitivity chart
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Bits per Pixel – Scanned Pictures
8 bits / pixel / color 6 bits / pixel / color
Marc Levoy / Hanna-Barbera
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Bits per Pixel – Scanned Pictures (cont.)
5 bits / pixel / color 4 bits / pixel / color
Marc Levoy / Hanna-Barbera
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Bits per Pixel – Line Drawings
8 bits / pixel / color 4 bits / pixel / color
Marc Levoy / Hanna-Barbera
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Bits per Pixel – Line Drawings (cont.)
3 bits / pixel / color 2 bits / pixel / color
Marc Levoy / Hanna-Barbera
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Seurat: The Side Show, 1888
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Color
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Aguilonius, 1613
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Newton: color circle from Optiks, 1704
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Johann Lambert: Color pyramid, 1772
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Runge: Colour Sphere, 1809
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Modern Understanding of Color
• Two types of receptors: rods and cones
Rods and cones Cones in fovea
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Rods and Cones
• Rods – More sensitive in low light: “scotopic” vision
– More dense near periphery
• Cones – Only function with higher light levels:
“photopic” vision
– Densely packed at center of eye: fovea
– Different types of cones → color vision
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Electromagnetic Spectrum
• Visible light frequencies range between ... – Red = 4.3 x 1014 hertz (700nm)
– Violet = 7.5 x 1014 hertz (400nm)
Figures 15.1 from H&B
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Visible Light
• Color may be characterized by … – Hue = dominant frequency (highest peak)
– Saturation = excitation purity (ratio of highest to rest)
– Lightness = luminance (area under curve)
White Light Orange Light Figures 15.3-4 from H&B
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Color Perception
Figure 13.18 from FvDFH
Spectral-response functions of the three types of cones.
Tristimulus theory of color
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Tristimulus Color
• Any distribution of light can be summarized by its effect on 3 types of cones
• Therefore, human perception of color is a 3-dimensional space
• Metamerism: different spectra, same response
• Color blindness: fewer than 3 types of cones – Most commonly L cone = M cone
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Color Models
• RGB
• XYZ
• CMY
• HSV
• …etc
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Color Models
• Different ways of parameterizing 3D space
• RGB – Official standard:
R = 645.16 nm, G = 526.32 nm, B = 444.44 nm
– Most monitors are some approximation to this
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Color CRT
Figure 2.8 from H&B
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Color LCD
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RGB Color Model
Plate II.3 from FvDFH
R G B Color 0.0 0.0 0.0 Black 1.0 0.0 0.0 Red 0.0 1.0 0.0 Green 0.0 0.0 1.0 Blue 1.0 1.0 0.0 Yellow 1.0 0.0 1.0 Magenta 0.0 1.0 1.0 Cyan 1.0 1.0 1.0 White 0.5 0.0 0.0 ? 1.0 0.5 0.5 ? 1.0 0.5 0.0 ? 0.5 0.3 0.1 ?
Colors are additive
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RGB Color Cube
Figures 15.11&15.12 from H&B
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RGB Spectral Colors
Amounts of RGB primaries needed to display spectral colors
? Figure 15.5 from H&B
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XYZ Color Model (CIE)
Amounts of CIE primaries needed to display spectral colors
Figure 15.6 from H&B
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XYZ Colorspace
• RGB can’t represent all pure wavelengths with positive values – Saturated greens would require negative red
• XYZ colorspace is a linear transform of RGB so that all pure wavelengths have positive values
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CIE Chromaticity Diagram
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CIE Chromaticity Diagram
Normalized amounts of X and Y for colors in visible spectrum
Figure 15.7 from H&B
(White)
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CIE Chromaticity Diagram
Figures 15.8-10 from H&B
Compare Color
Gamuts
Identify Complementary
Colors
Determine Dominant Wavelength
and Purity
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RGB Color Gamut
Color gamut for a typical RGB computer monitor
Figure 15.13 from H&B
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CMY Color Model
Plate II.7 from FvDFH
C M Y Color 0.0 0.0 0.0 White 1.0 0.0 0.0 Cyan 0.0 1.0 0.0 Magenta 0.0 0.0 1.0 Yellow 1.0 1.0 0.0 Blue 1.0 0.0 1.0 Green 0.0 1.0 1.0 Red 1.0 1.0 1.0 Black 0.5 0.0 0.0 ? 1.0 0.5 0.5 ? 1.0 0.5 0.0 ?
Colors are subtractive
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CMY Color Cube
Figure 15.14 from H&B
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HSV Color Model
Figure 15.16&15.17 from H&B
H S V Color 0 1.0 1.0 Red 120 1.0 1.0 Green 240 1.0 1.0 Blue * 0.0 1.0 White * 0.0 0.5 Gray * * 0.0 Black 60 1.0 1.0 ? 270 0.5 1.0 ? 270 0.0 0.7 ?
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Colorspaces for Television
• Differences in brightness more important than differences in color
• YCrCb, YUV, YIQ colorspaces = linear transforms of RGB – Lightness: Y=0.299R+0.587G+0.114B
– Other color components typically allocated less bandwidth than Y
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Perceptually-Uniform Colorspaces
• Most colorspaces not perceptually uniform
• MacAdam ellipses: color within each ellipse appears constant (shown here 10X size)
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Perceptually-Uniform Colorspaces
• u’v’ space
• Not perfect, but better than XYZ
ZYXYv
ZYXXu
3159'
3154'
++=
++=
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L*a*b* Color Space
• Another choice: L*a*b*
−
=
−
=
−
=
3/13/1
3/13/1
3/1
200*
500*
16116*
nn
nn
n
ZZ
YYb
YY
XXa
YYL
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L*a*b* Color Space
• Often used for color comparison when “perceptual” differences matter
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Summary
• Perception and representation of – Intensity, frequency, color
• Color – Tristimulus theory of color
– CIE Chromaticity Diagram
– Different color models
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Preattentive Processing
• Some properties are processed preattentively (without need for focusing attention).
• Important for art, design of visualizations – what can be perceived immediately
– what properties are good discriminators
– what can mislead viewers
Preattentive processing sildes from Healey http://www.csc.ncsu.edu/faculty/healey/PP/PP.html
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Example: Color Selection
Viewer can rapidly and accurately determine whether the target (red circle) is present or absent. Difference detected in color.
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Example: Shape Selection
Viewer can rapidly and accurately determine whether the target (red circle) is present or absent. Difference detected in form (curvature)
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Pre-attentive Processing
• < 200–250 ms qualifies as pre-attentive – eye movements take at least 200ms
– yet certain processing can be done very quickly, implying low-level processing in parallel
• If a decision takes a fixed amount of time regardless of the number of distractors, it is considered to be preattentive
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Example: Conjunction of Features
Viewer cannot rapidly and accurately determine whether the target (red circle) is present or absent when target has two or more features, each of which are present in the distractors. Viewer must search sequentially.
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Example: Emergent Features
Target has a unique feature with respect to distractors (open sides) and so the group can be detected preattentively.
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Example: Emergent Features
Target does not have a unique feature with respect to distractors and so the group cannot be detected preattentively.
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Asymmetric and Graded Preattentive Properties
• Some properties are asymmetric – a sloped line among vertical lines is preattentive
– a vertical line among sloped ones is not
• Some properties have a gradation – some more easily discriminated among than others
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SUBJECT PUNCHED QUICKLY OXIDIZED TCEJBUS DEHCNUP YLKCIUQ DEZIDIXO CERTAIN QUICKLY PUNCHED METHODS NIATREC YLKCIUQ DEHCNUP SDOHTEM SCIENCE ENGLISH RECORDS COLUMNS ECNEICS HSILGNE SDROCER SNMULOC GOVERNS PRECISE EXAMPLE MERCURY SNREVOG ESICERP ELPMAXE YRUCREM CERTAIN QUICKLY PUNCHED METHODS NIATREC YLKCIUQ DEHCNUP SDOHTEM GOVERNS PRECISE EXAMPLE MERCURY SNREVOG ESICERP ELPMAXE YRUCREM SCIENCE ENGLISH RECORDS COLUMNS ECNEICS HSILGNE SDROCER SNMULOC SUBJECT PUNCHED QUICKLY OXIDIZED TCEJBUS DEHCNUP YLKCIUQ DEZIDIXO CERTAIN QUICKLY PUNCHED METHODS NIATREC YLKCIUQ DEHCNUP SDOHTEM SCIENCE ENGLISH RECORDS COLUMNS ECNEICS HSILGNE SDROCER SNMULOC
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SUBJECT PUNCHED QUICKLY OXIDIZED TCEJBUS DEHCNUP YLKCIUQ DEZIDIXO CERTAIN QUICKLY PUNCHED METHODS NIATREC YLKCIUQ DEHCNUP SDOHTEM SCIENCE ENGLISH RECORDS COLUMNS ECNEICS HSILGNE SDROCER SNMULOC GOVERNS PRECISE EXAMPLE MERCURY SNREVOG ESICERP ELPMAXE YRUCREM CERTAIN QUICKLY PUNCHED METHODS NIATREC YLKCIUQ DEHCNUP SDOHTEM GOVERNS PRECISE EXAMPLE MERCURY SNREVOG ESICERP ELPMAXE YRUCREM SCIENCE ENGLISH RECORDS COLUMNS ECNEICS HSILGNE SDROCER SNMULOC SUBJECT PUNCHED QUICKLY OXIDIZED TCEJBUS DEHCNUP YLKCIUQ DEZIDIXO CERTAIN QUICKLY PUNCHED METHODS NIATREC YLKCIUQ DEHCNUP SDOHTEM SCIENCE ENGLISH RECORDS COLUMNS ECNEICS HSILGNE SDROCER SNMULOC
Text NOT Preattentive
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Preattentive Visual Properties [Healey 97]
length Triesman & Gormican [1988]
width Julesz [1985]
size Triesman & Gelade [1980]
curvature Triesman & Gormican [1988]
number Julesz [1985]; Trick & Pylyshyn [1994]
terminators Julesz & Bergen [1983]
intersection Julesz & Bergen [1983]
closure Enns [1986]; Triesman & Souther [1985]
colour (hue) Nagy & Sanchez [1990, 1992]; D'Zmura [1991] Kawai et al. [1995]; Bauer et al. [1996]
intensity Beck et al. [1983]; Triesman & Gormican [1988]
flicker Julesz [1971]
direction of motion Nakayama & Silverman [1986]; Driver & McLeod [1992]
binocular lustre Wolfe & Franzel [1988]
stereoscopic depth Nakayama & Silverman [1986]
3-D depth cues Enns [1990]
lighting direction Enns [1990]
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Accuracy Ranking of Quantitative Perceptual Tasks Estimated; only pairwise comparisons have been validated
[Mackinlay 88 from Cleveland & McGill]
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Visual Illusions
• People don’t perceive length, area, angle, brightness they way they “should”
• Some illusions have been reclassified as systematic perceptual errors – e.g., brightness contrasts (grey square on
white background vs. on black background)
– partly due to increase in our understanding of the relevant parts of the visual system
• Nevertheless, the visual system does some really unexpected things
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Illusions of Linear Extent
• Mueller-Lyon (off by 25-30%)
• Horizontal-Vertical
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Illusions of Area
• Delboeuf Illusion
• Height of 4-story building overestimated by approximately 25%