vision and color theory
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Vision and Color Theory. Haim Levkowitz Institute for Visualization and Perception Research Department of Computer Science University of Massachusetts Lowell. Topics. Human & Color Vision Color vs. Luminance Systems Color Deficiencies Color Organization Modeling Complex Visual Themes - PowerPoint PPT PresentationTRANSCRIPT
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Institute for Visualization and Perception ResearchI VP R 1
© Copyright 1998 Haim Levkowitz
Vision and Color Theory
Haim Levkowitz
Institute for Visualization and Perception Research
Department of Computer Science
University of Massachusetts Lowell
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Topics ...
• Human & Color Vision
• Color vs. Luminance Systems
• Color Deficiencies
• Color Organization Modeling
• Complex Visual Themes
• Device Independent Color Displays
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Newton, Opticks 1704, pp. 124-125.• "the rays, to speak properly, are
not coloured. In them there is nothing else than a certain Power and Disposition to stir up a Sensation of this or that Colour."
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The Human Interface• Visual technologies matched to human
visual capabilities
• Print evolved over centuries
• Electronic display revolution-CRT terminals ==> virtual reality in 30 years!
• Requirements for image quality …
• Techn. q's rely on visual percep. of human observer ...
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© Copyright 1998 Haim Levkowitz
Requirements for image quality ...
• Sufficient luminance and contrast• No flicker• Effective use of color• Minimized effects of spatial sampling• Perceptually lossless image
compression• Convincing impression of depth
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© Copyright 1998 Haim Levkowitz
Techn. q's rely on visual percep. of human observer ...• How do humans process luminance,
contrast, color, motion?
• How do these mechanisms constrain choice of capture, sample, compress, and display information?
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© Copyright 1998 Haim Levkowitz
Some Definitions
• Physical stimulus …
• Perception …
• Psychophysics …
• Neurophysiology ...
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© Copyright 1998 Haim Levkowitz
Physical stimulus ...
• Measurable properties of the physical world
• Luminance
• Sound pressure
• Wavelength
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Perception ...
• Study sensory phenomena
• Mediated by higher-level processes
• Memory
• Attention
• Experience
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© Copyright 1998 Haim Levkowitz
Psychophysics ...
• Study sensations/perceptions physical energies produce
• Brightness
• Loudness
• Color
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Neurophysiology ...
• Study physiological mechanisms mediating sensory information
• Transduction
• Coding
• Communication
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© Copyright 1998 Haim Levkowitz
Color: Physics vs. Perception
Luminance
Dominant wavelength
Purity
Physical Perceptual
lightness (objects; reflected light)
hue (hue circle; red vs. purple, etc.)
saturation (vivid vs. pastel)
brightness (light sources; emitted light)
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An Overview of the Human Visual System
• The Eye
• Schematic of the eye ...
• Two lenses-one fixed (cornea); one variable (lens)
• Pupil-operates like camera aperture
• Retina …
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© Copyright 1998 Haim Levkowitz
Schematic of the eye ...
Lens
Corneaa
Fovea Muscle
Optic nerve
Retina
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Retina ...
• 5 layers of cells at back of eye
• Photoreceptors-light sensitive cells …
• 4 other classes of cells …
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© Copyright 1998 Haim Levkowitz
Photoreceptors-light sensitive cells …
• Rods (~120 million) …
• Cones (~8 million) …
• No photoreceptors in the optic disk
• ==> blind spot
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© Copyright 1998 Haim Levkowitz
Rods (~120 million) ...
• Achromatic
• Night/low light levels
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© Copyright 1998 Haim Levkowitz
Cones (~8 million) ...
• Daytime color vision/color coding
• Major concentration in fovea
• (central one degree of vision)
• 3 wavelength distributions ...
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© Copyright 1998 Haim Levkowitz
3 wavelength distributions ...
• S: short-violet ("blue")
• Peak sensitivity 440 nm
• M: medium-yellowish-green ("green")
• 550 nm
• L : long-yellow ("red")
• 570 nm
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© Copyright 1998 Haim Levkowitz
4 other classes of cells ...
• Image compression
• Lateral inhibition
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© Copyright 1998 Haim Levkowitz
Eye Movements• Saccades (4/sec.)
• 6 muscles point eye to areas of interest
• Clear stable impression of external world
• Minisaccades
• Keep eye in const. movement
• Req'd for perception
• If not, external world fades away
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© Copyright 1998 Haim Levkowitz
Sensitivity vs. Resolution
• 1-1 mapping receptors to ganglion cells
• ==> highest spatial resolution (acuity)
• Fovea
• Many-1 mapping
• ==> highest sensitivity
• Periphery
• Greater temporal sensitivity
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© Copyright 1998 Haim Levkowitz
60% of brain receives visual input
• Input to decision making, memory, and concept formation processes
• Looking at a display involves ...
• More than questions of image quality and detectability
• How seek out, understand, and use information.
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© Copyright 1998 Haim Levkowitz
Basic Visual Mechanisms
• Early Vision: Luminance Perception …
• Contrast and spatial resolution
• Image applications of CSF
• Perceived flicker
• Inexpesive way to reduce perceived flicker ...
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© Copyright 1998 Haim Levkowitz
Early Vision: Luminance Perception …
• Sensitivity to luminance variations …
• Apparent brightness: not linear function of luminance ...
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© Copyright 1998 Haim Levkowitz
Sensitivity to luminance variations …
• 14 log-unit range: dim star --> bright sunlight …
• At any moment, a 2 log-unit range
• Matched to ambient illumination
• Dynamics of light-and- dark-adaptation
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© Copyright 1998 Haim Levkowitz
14 log-unit range: dim star --> bright sunlight ...
• Sun at noon: 108-1010 candles/meter2 (Damaging)• Photopic
• Filament of 100 watt light bulb: 107
• Comfortable reading: 10• Mixed (mesopic): 1• Scotopoic
• White paper in moonlight: 10-2
• Weakest visible light: 10-6
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© Copyright 1998 Haim Levkowitz
Apparent brightness: not linear function of luminance ...• Non-linear psychophysical
relationship ...
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© Copyright 1998 Haim Levkowitz
Non-linear psychophysical relationship ...• Reflects visual coding
• Perceptual
• "gamma" function
• "Logarithmic"
• range
• Incorporated
• in many color metrics
Luminance
Apparent Brightness
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© Copyright 1998 Haim Levkowitz
Contrast and spatial resolution
• Visual angle …
• Contrast sensitivity …
• Minimum modulation to detect grating patterns ...
Contrast =Luminance(object)
Luminance(background)
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© Copyright 1998 Haim Levkowitz
Visual angle ...
• For some objects ...
tan /2 = S/2DS = sizeD = distance
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© Copyright 1998 Haim Levkowitz
For some objects ...
• Character on CRT at 50 cm: 17'• Diameter of sun, moon: 30'• Lower case pica-type letter at reading distance 40 cm: 13'• Quarter at arm's length, 90 yards, 3 miles: 20, 1', 1"• Diameter of fovea: 10• Diameter of foveal receptor: 30"• Position of inner edge of blind spot: 120 from fovea• Size of blind spot: 7.50 (v), 50 (h)
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© Copyright 1998 Haim Levkowitz
Contrast sensitivity ...
• Depends on spatial distribution of light and dark
RelativeContrast Sensitivity (dB)
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© Copyright 1998 Haim Levkowitz
Minimum modulation to detect grating patterns ...
• Tuned function of spatial frequency
• Peak sensitivity at 2-4 cycles/degree
• Decreased sensitivity for• Lower (broader)• Higher (finer) spatial
frequency
RelativeContrast Sensitivity (dB)
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© Copyright 1998 Haim Levkowitz
Image Applications of CSF
• Application areas ...
• CSF caveats ...
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Application Areas ...
• Image coding …
• Digital halftoning …
• Display quality ...
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© Copyright 1998 Haim Levkowitz
Image coding ...
• Gain efficiency
• Greatest BW to regions of greatest spatial frequency sensitivity
RelativeContrast Sensitivity (dB)
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© Copyright 1998 Haim Levkowitz
Digital halftoning ...
• Hide sampling noise in regions of lowest contrast sensitivity
Spatial Frequency (cycles/deg)
RelativeContrast Sensitivity (dB)
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Display quality ...
• Evaluate display modulation transfer function (MTF) with human contrast sensitivity function (CSF)
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© Copyright 1998 Haim Levkowitz
CSF Caveats ...
• Depends on
• Luminance
• Color
• Temporal modulation
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© Copyright 1998 Haim Levkowitz
Perceived flicker
• Depends on luminance, not color
• Factors affecting flicker ...
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© Copyright 1998 Haim Levkowitz
Factors affecting flicker …
• Display parameters ...
• Viewing conditions
• Peripheral vs. foveal gaze
• Observer factors
• Age, caffeine, depressants
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© Copyright 1998 Haim Levkowitz
Display parameters ...
• Luminance
• Refresh rate
• Interlaced vs. non-interlaced
• Phosphor persistence
• Display size
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© Copyright 1998 Haim Levkowitz
Inexpensive way to reduce perceived flicker ...
• Add dark glass, "anti-glare" faceplate
• Reduce luminance
• ==> Reduce perceived flicker
• Increase contrast
• Ambient light attenuated twice
• Emitted light attenuated once
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© Copyright 1998 Haim Levkowitz
Introduction to human color vision
• Color vision: Trichromacy …
• Implications of trichromacy
• Second stage: Opponent Processes
• Color deficiencies
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© Copyright 1998 Haim Levkowitz
Color vision: Trichromacy ...
• Three broadband filters
• Tuned to three ranges of wavelength
• "Blue," "green," "red"-misnomers! …
• Perceived hue ...
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"Blue," "green," "red"-misnomers! …
• Each one color blind …
• "Blue"
• Peaks at "blue" wavelength
• "Green" and "red"
• Largely overlapping
• Peak at roughly "yellow"
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Each one color blind …
b: “Green”
Wavelength (nm)
Fraction of lightabsorbed by eachtype of cone
400 680440 480 520 560 600 640
.20
.18
.16
.14
.12
0
.10
.08
.06
.04
.02
g: “Red”
a: “Blue”
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Perceived hue …
• Combination across three cone mechanisms …
• Example …
• Metamerism ...
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Combination across three cone mechanisms ...
b: “Green”
Wavelength (nm)
Fraction of lightabsorbed by eachtype of cone
400 680440 480 520 560 600 640
.20
.18
.16
.14
.12
0
.10
.08
.06
.04
.02
g: “Red”
a: “Blue”
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Example ...
• Equal excitation
• to medium
• and long
• ==> "yellow"
Medium
Wavelength (nm)400 680440 480 520 560 600 640
.20
.18
.16
.14
.12
0
.10
.08
.06
.04
.02
Long
Short
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Metamerism ...
• Solution 1
• 550 nm monochromatic
• Solution 2
• 530 nm + 630 nm
• Equivalent sensation
Medium
Wavelength (nm)400 680440 480 520 560 600 640
.20
.18
.16
.14
.12
0
.10
.08
.06
.04
.02
Long
Short
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Implications of trichromacy
• Any hue
• Matched by combination of 3 primaries
• Produced by infinite number of wavelength combinations
• Basis for color TV and VDT technology ...
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Basis for color TV and VDT technology ...
• Very efficient-need only 3 primaries to produce millions of colors
• E.g., does not require "yellow" gun
• Primaries define gamut of colors
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Second Stage: Opponent Processes
• Recombine photoreceptor outputs into 3 opponent channels
R – G Y – BW – Bk
“Red” “Green” “Blue”
Y = R + GRG
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Color deficiencies
• ~8% males, < 1% females have some genetic color deficiency
• Most common: deuteranomaly …
• Others …
• Color deficiencies and visual displays ...
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Most common: deuteranomaly ...
• 5% males, 0.5% females
• Anomalous trichromacy
• Caused by abnormal M-type cone
• ==> abnormal matches/poor discrimination
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Others …
• Missing/abnormal cone type …
• Much less common
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Missing/abnormal cone type ...
• E.g.,
• Abnormal M-type …
• Abnormal L-type ...
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Abnormal M-type ...
Normal
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Abnormal L-type ...
Normal
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Color deficiencies and visual displays ...
• Color deficient people may not be aware
• Simple rule-of-thumb ...
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© Copyright 1998 Haim Levkowitz
Simple rule-of-thumb ...• Code important distinctions with
redundancy
• Luminance
• E.g., spell-checker highlights misspelled words
• Red
• Bright
• Size
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Institute for Visualization and Perception ResearchI VP R 64
© Copyright 1998 Haim Levkowitz
Color-luminance interactions
• Luminance vs. color …
• The Luminance Mechanism …
• The Color System ...
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Institute for Visualization and Perception ResearchI VP R 65
© Copyright 1998 Haim Levkowitz
Luminance vs. color ...
• Detect chromatic & achromatic grating patterns
• As function
• of spatial frequency
Blue/Yellow
Red/Green
RelativeContrast Sensitivity (dB)
Black/White
Spatial Frequency (cycles/deg)
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Institute for Visualization and Perception ResearchI VP R 66
© Copyright 1998 Haim Levkowitz
The Luminance Mechanism …
• Mediates high spatial-frequency tasks …
• Has broader bandwidth ...
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Institute for Visualization and Perception ResearchI VP R 67
© Copyright 1998 Haim Levkowitz
Mediates high spatial-frequency tasks ...
• Yellow text on white: hard• Little lum. diff. for high spat.-freq. task• Luminance system has nothing to work
with• High spatial resolution
• Depends on luminance• Independent of hue
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Institute for Visualization and Perception ResearchI VP R 68
© Copyright 1998 Haim Levkowitz
Has broader bandwidth ...
• More BW to encode luminance spatial variations
• Adding color to TV: little add'l BW
• Color info. in between bands
• Compression schemes
• Most BW to luminance
• ==> higher image quality w/fewer bits
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Institute for Visualization and Perception ResearchI VP R 69
© Copyright 1998 Haim Levkowitz
The Color System ...
• More sensitive to low spatial frequencies
• Small color targets achromatic …
• Large color areas more saturated & intense ...
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Institute for Visualization and Perception ResearchI VP R 70
© Copyright 1998 Haim Levkowitz
Small color targets achromatic ...
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Institute for Visualization and Perception ResearchI VP R 71
© Copyright 1998 Haim Levkowitz
Large color areas more saturated & intense …
• Small "pale salmon" paint chip …
• Looks like "mango frenzy" on a large wall ...
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Institute for Visualization and Perception ResearchI VP R 72
© Copyright 1998 Haim Levkowitz
Small "pale salmon" paint chip ...
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Institute for Visualization and Perception ResearchI VP R 73
© Copyright 1998 Haim Levkowitz
Looks like "mango frenzy" on a large wall ...
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Institute for Visualization and Perception ResearchI VP R 74
© Copyright 1998 Haim Levkowitz
Introduction to color calibration
• Device independent color …
• Selecting the correct color space …
• Cross rendering ...
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Institute for Visualization and Perception ResearchI VP R 75
© Copyright 1998 Haim Levkowitz
Device independent color …
• Definition …
• Steps to device independent color ...
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Institute for Visualization and Perception ResearchI VP R 76
© Copyright 1998 Haim Levkowitz
Definition ...
• Color images look comparable
• On CRT
• Projected
• On printer
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Institute for Visualization and Perception ResearchI VP R 77
© Copyright 1998 Haim Levkowitz
Steps to device independent color ...
• Calibrate output devices
• Luminance values?
• Output device calibration …
• Transform to appropriate color metric
• Choose correct optimization
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Institute for Visualization and Perception ResearchI VP R 78
© Copyright 1998 Haim Levkowitz
Output device calibration ...• D/A values to R, G, B guns?
• Not good enough!
• Measure luminance response function for each gun
• Straightforward
• Benefits of calibration …
• Why (uncalibrated) RGB is dangerous ...
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Institute for Visualization and Perception ResearchI VP R 79
© Copyright 1998 Haim Levkowitz
Benefits of calibration ...
• Express image chromaticities in standard metric spaces
• Manipulate color in systematic ways
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Institute for Visualization and Perception ResearchI VP R 80
© Copyright 1998 Haim Levkowitz
Why (uncalibrated) RGB is dangerous ...
• If R, G, B values are D/A values, not luminance values, transformations from RGB to any metric space will be uninterpretable
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Institute for Visualization and Perception ResearchI VP R 81
© Copyright 1998 Haim Levkowitz
Selecting the correct color space …
• Colorimetric approaches …
• Spaces for additive vs. subtractive color devices
• "Uniform Color Spaces" …
• Color naming
• Color constancy
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Institute for Visualization and Perception ResearchI VP R 82
© Copyright 1998 Haim Levkowitz
Colorimetric approaches ...
• Based on color matching data
• Commission Internationale De L'Eclairage (CIE)
• CIE 1931
• Linear transformations of CIE 1931
• Taking luminance into account
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Institute for Visualization and Perception ResearchI VP R 83
© Copyright 1998 Haim Levkowitz
"Uniform Color Spaces" ...
• Pseudo perceptual• Generalized LHS model (Levkowitz)
• Perceptual• Incorporating Opponent Process
mechanisms• Post-receptor processes plus gain
control (Guth)
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Institute for Visualization and Perception ResearchI VP R 84
© Copyright 1998 Haim Levkowitz
Cross rendering ...
• Color information from one device to another
• Difficult: "gamut mismatch" …
• Solving the gamut mismatch problem …
• Answer from psychophysics
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Institute for Visualization and Perception ResearchI VP R 85
© Copyright 1998 Haim Levkowitz
Difficult: "gamut mismatch" ...
• Devices operate at different• Luminance levels• Range of possible hues depends on
luminance• Pigments/phosphors
• ==> Range (gamut) of colors not fully overlapping
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Institute for Visualization and Perception ResearchI VP R 86
© Copyright 1998 Haim Levkowitz
Solving the gamut mismatch problem …
• Pixel-by-pixel chromaticity match …• Selective color preservation based on
image content• Color name preservation• Role of color constancy
• Appearance-preserving uniform transformations?
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Institute for Visualization and Perception ResearchI VP R 87
© Copyright 1998 Haim Levkowitz
Pixel-by-pixel chromaticity match ...
• "Best" match? closest in
• hue, saturation, lightness, some combination?
• In what metric space?
• What combination rule best describes fit?
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Institute for Visualization and Perception ResearchI VP R 88
© Copyright 1998 Haim Levkowitz
Color vision for complex visual tasks
• Color and visual search …
• Visual/verbal interactions
• Color contrast and color constancy
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Institute for Visualization and Perception ResearchI VP R 89
© Copyright 1998 Haim Levkowitz
Color and visual search …
• Color attracts "pre-attentive" vision …
• Applications …
• Theoretic basis: Treisman 80 …
• Color for feature coding: caveats ...
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Institute for Visualization and Perception ResearchI VP R 90
© Copyright 1998 Haim Levkowitz
Color attracts "pre-attentive" vision ...
• Attracts attention
• Facilitates effortless search
• Enables parallel search
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Institute for Visualization and Perception ResearchI VP R 91
© Copyright 1998 Haim Levkowitz
Applications ...
• Draw attention to features of interest
• Identify
• Red car
• Colorfully-dressed child
• Scan for multiple instances of feature
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Institute for Visualization and Perception ResearchI VP R 92
© Copyright 1998 Haim Levkowitz
Theoretic basis: Treisman 80 …
• "Popout" effect in visual search …
• Most salient "popout" features ...
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Institute for Visualization and Perception ResearchI VP R 93
© Copyright 1998 Haim Levkowitz
"Popout" effect in visual search ...
• Time to detect presence of target• Disjunctive target (single feature)
• Parallel: Ind. of # of items • Targets based on conjunction of
features• Serial: linear w/number of items
• Treisman and Gelade (1980) ...
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Institute for Visualization and Perception ResearchI VP R 94
© Copyright 1998 Haim Levkowitz
Treisman and Gelade (1980) ...
Number of Display Items
Time to DetectTarget(Msec)
Conjunctive (green "T")Disjunctive (blue letter)Disjunctive ("S")
1200
800
400
1 5 15 30
T
TS
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Institute for Visualization and Perception ResearchI VP R 95
© Copyright 1998 Haim Levkowitz
Most salient "popout" features ...
• Color
• Depth
• [Nakayama, 1986]
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Institute for Visualization and Perception ResearchI VP R 96
© Copyright 1998 Haim Levkowitz
Color for feature coding: caveats …
• Preattentive features can distract …• Or attract …• Engage powerful preattentive aspects
for trivial ends• Role can be overstated
• Processes requiring attention & analysis also important
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Institute for Visualization and Perception ResearchI VP R 97
© Copyright 1998 Haim Levkowitz
Preattentive features can distract ...
NYMXOLORSEZPGRDBEINGNP
HZMAYTQCJMAK
MWHAIJSE
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Institute for Visualization and Perception ResearchI VP R 98
© Copyright 1998 Haim Levkowitz
Or attract ...
NYMXOLORSEZPGSYBSEINGNP
HZMAYTQCJMAK
MWEAIJSE
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Institute for Visualization and Perception ResearchI VP R 99
© Copyright 1998 Haim Levkowitz
The Stroop Effect (1935) ...
• Task: name color of words
• Nonsense words
• Color naming very fast
• Color names
• Color naming very slow
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Institute for Visualization and Perception ResearchI VP R 100
© Copyright 1998 Haim Levkowitz
The Stroop Effect (1935) ...
BLUEGREENREDYELLOWREDYELLOWGREENBLUE
YELLOWBLUEREDGREENREDBLUEYELLOWGREEN
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Institute for Visualization and Perception ResearchI VP R 101
© Copyright 1998 Haim Levkowitz
Color contrast and color constancy …
• Color contrast ...
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Institute for Visualization and Perception ResearchI VP R 102
© Copyright 1998 Haim Levkowitz
Color contrast …
• Perceived hue depends on surrounding hues …
• Induction of complementary hue …
• Mediated by opponent process mechanisms
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Institute for Visualization and Perception ResearchI VP R 103
© Copyright 1998 Haim Levkowitz
Perceived hue depends on surrounding hues ...
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Institute for Visualization and Perception ResearchI VP R 104
© Copyright 1998 Haim Levkowitz
Induction of complementary hue ...