vision and color theory

104
Institute for Visualization and Perception Research IV P 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

Upload: preston-bishop

Post on 30-Dec-2015

64 views

Category:

Documents


3 download

DESCRIPTION

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 Presentation

TRANSCRIPT

Page 1: Vision and Color Theory

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

Page 2: Vision and Color Theory

Institute for Visualization and Perception ResearchI VP R 2

© Copyright 1998 Haim Levkowitz

Topics ...

• Human & Color Vision

• Color vs. Luminance Systems

• Color Deficiencies

• Color Organization Modeling

• Complex Visual Themes

• Device Independent Color Displays

Page 3: Vision and Color Theory

Institute for Visualization and Perception ResearchI VP R 3

© Copyright 1998 Haim Levkowitz

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."

Page 4: Vision and Color Theory

Institute for Visualization and Perception ResearchI VP R 4

© Copyright 1998 Haim Levkowitz

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 ...

Page 5: Vision and Color Theory

Institute for Visualization and Perception ResearchI VP R 5

© 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

Page 6: Vision and Color Theory

Institute for Visualization and Perception ResearchI VP R 6

© 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?

Page 7: Vision and Color Theory

Institute for Visualization and Perception ResearchI VP R 7

© Copyright 1998 Haim Levkowitz

Some Definitions

• Physical stimulus …

• Perception …

• Psychophysics …

• Neurophysiology ...

Page 8: Vision and Color Theory

Institute for Visualization and Perception ResearchI VP R 8

© Copyright 1998 Haim Levkowitz

Physical stimulus ...

• Measurable properties of the physical world

• Luminance

• Sound pressure

• Wavelength

Page 9: Vision and Color Theory

Institute for Visualization and Perception ResearchI VP R 9

© Copyright 1998 Haim Levkowitz

Perception ...

• Study sensory phenomena

• Mediated by higher-level processes

• Memory

• Attention

• Experience

Page 10: Vision and Color Theory

Institute for Visualization and Perception ResearchI VP R 10

© Copyright 1998 Haim Levkowitz

Psychophysics ...

• Study sensations/perceptions physical energies produce

• Brightness

• Loudness

• Color

Page 11: Vision and Color Theory

Institute for Visualization and Perception ResearchI VP R 11

© Copyright 1998 Haim Levkowitz

Neurophysiology ...

• Study physiological mechanisms mediating sensory information

• Transduction

• Coding

• Communication

Page 12: Vision and Color Theory

Institute for Visualization and Perception ResearchI VP R 12

© 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)

Page 13: Vision and Color Theory

Institute for Visualization and Perception ResearchI VP R 13

© Copyright 1998 Haim Levkowitz

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 …

Page 14: Vision and Color Theory

Institute for Visualization and Perception ResearchI VP R 14

© Copyright 1998 Haim Levkowitz

Schematic of the eye ...

Lens

Corneaa

Fovea Muscle

Optic nerve

Retina

Page 15: Vision and Color Theory

Institute for Visualization and Perception ResearchI VP R 15

© Copyright 1998 Haim Levkowitz

Retina ...

• 5 layers of cells at back of eye

• Photoreceptors-light sensitive cells …

• 4 other classes of cells …

Page 16: Vision and Color Theory

Institute for Visualization and Perception ResearchI VP R 16

© Copyright 1998 Haim Levkowitz

Photoreceptors-light sensitive cells …

• Rods (~120 million) …

• Cones (~8 million) …

• No photoreceptors in the optic disk

• ==> blind spot

Page 17: Vision and Color Theory

Institute for Visualization and Perception ResearchI VP R 17

© Copyright 1998 Haim Levkowitz

Rods (~120 million) ...

• Achromatic

• Night/low light levels

Page 18: Vision and Color Theory

Institute for Visualization and Perception ResearchI VP R 18

© Copyright 1998 Haim Levkowitz

Cones (~8 million) ...

• Daytime color vision/color coding

• Major concentration in fovea

• (central one degree of vision)

• 3 wavelength distributions ...

Page 19: Vision and Color Theory

Institute for Visualization and Perception ResearchI VP R 19

© 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

Page 20: Vision and Color Theory

Institute for Visualization and Perception ResearchI VP R 20

© Copyright 1998 Haim Levkowitz

4 other classes of cells ...

• Image compression

• Lateral inhibition

Page 21: Vision and Color Theory

Institute for Visualization and Perception ResearchI VP R 21

© 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

Page 22: Vision and Color Theory

Institute for Visualization and Perception ResearchI VP R 22

© 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

Page 23: Vision and Color Theory

Institute for Visualization and Perception ResearchI VP R 23

© 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.

Page 24: Vision and Color Theory

Institute for Visualization and Perception ResearchI VP R 24

© 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 ...

Page 25: Vision and Color Theory

Institute for Visualization and Perception ResearchI VP R 25

© Copyright 1998 Haim Levkowitz

Early Vision: Luminance Perception …

• Sensitivity to luminance variations …

• Apparent brightness: not linear function of luminance ...

Page 26: Vision and Color Theory

Institute for Visualization and Perception ResearchI VP R 26

© 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

Page 27: Vision and Color Theory

Institute for Visualization and Perception ResearchI VP R 27

© 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

Page 28: Vision and Color Theory

Institute for Visualization and Perception ResearchI VP R 28

© Copyright 1998 Haim Levkowitz

Apparent brightness: not linear function of luminance ...• Non-linear psychophysical

relationship ...

Page 29: Vision and Color Theory

Institute for Visualization and Perception ResearchI VP R 29

© Copyright 1998 Haim Levkowitz

Non-linear psychophysical relationship ...• Reflects visual coding

• Perceptual

• "gamma" function

• "Logarithmic"

• range

• Incorporated

• in many color metrics

Luminance

Apparent Brightness

Page 30: Vision and Color Theory

Institute for Visualization and Perception ResearchI VP R 30

© Copyright 1998 Haim Levkowitz

Contrast and spatial resolution

• Visual angle …

• Contrast sensitivity …

• Minimum modulation to detect grating patterns ...

Contrast =Luminance(object)

Luminance(background)

Page 31: Vision and Color Theory

Institute for Visualization and Perception ResearchI VP R 31

© Copyright 1998 Haim Levkowitz

Visual angle ...

• For some objects ...

tan /2 = S/2DS = sizeD = distance

Page 32: Vision and Color Theory

Institute for Visualization and Perception ResearchI VP R 32

© 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)

Page 33: Vision and Color Theory

Institute for Visualization and Perception ResearchI VP R 33

© Copyright 1998 Haim Levkowitz

Contrast sensitivity ...

• Depends on spatial distribution of light and dark

RelativeContrast Sensitivity (dB)

Page 34: Vision and Color Theory

Institute for Visualization and Perception ResearchI VP R 34

© 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)

Page 35: Vision and Color Theory

Institute for Visualization and Perception ResearchI VP R 35

© Copyright 1998 Haim Levkowitz

Image Applications of CSF

• Application areas ...

• CSF caveats ...

Page 36: Vision and Color Theory

Institute for Visualization and Perception ResearchI VP R 36

© Copyright 1998 Haim Levkowitz

Application Areas ...

• Image coding …

• Digital halftoning …

• Display quality ...

Page 37: Vision and Color Theory

Institute for Visualization and Perception ResearchI VP R 37

© Copyright 1998 Haim Levkowitz

Image coding ...

• Gain efficiency

• Greatest BW to regions of greatest spatial frequency sensitivity

RelativeContrast Sensitivity (dB)

Page 38: Vision and Color Theory

Institute for Visualization and Perception ResearchI VP R 38

© Copyright 1998 Haim Levkowitz

Digital halftoning ...

• Hide sampling noise in regions of lowest contrast sensitivity

Spatial Frequency (cycles/deg)

RelativeContrast Sensitivity (dB)

Page 39: Vision and Color Theory

Institute for Visualization and Perception ResearchI VP R 39

© Copyright 1998 Haim Levkowitz

Display quality ...

• Evaluate display modulation transfer function (MTF) with human contrast sensitivity function (CSF)

Page 40: Vision and Color Theory

Institute for Visualization and Perception ResearchI VP R 40

© Copyright 1998 Haim Levkowitz

CSF Caveats ...

• Depends on

• Luminance

• Color

• Temporal modulation

Page 41: Vision and Color Theory

Institute for Visualization and Perception ResearchI VP R 41

© Copyright 1998 Haim Levkowitz

Perceived flicker

• Depends on luminance, not color

• Factors affecting flicker ...

Page 42: Vision and Color Theory

Institute for Visualization and Perception ResearchI VP R 42

© Copyright 1998 Haim Levkowitz

Factors affecting flicker …

• Display parameters ...

• Viewing conditions

• Peripheral vs. foveal gaze

• Observer factors

• Age, caffeine, depressants

Page 43: Vision and Color Theory

Institute for Visualization and Perception ResearchI VP R 43

© Copyright 1998 Haim Levkowitz

Display parameters ...

• Luminance

• Refresh rate

• Interlaced vs. non-interlaced

• Phosphor persistence

• Display size

Page 44: Vision and Color Theory

Institute for Visualization and Perception ResearchI VP R 44

© 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

Page 45: Vision and Color Theory

Institute for Visualization and Perception ResearchI VP R 45

© Copyright 1998 Haim Levkowitz

Introduction to human color vision

• Color vision: Trichromacy …

• Implications of trichromacy

• Second stage: Opponent Processes

• Color deficiencies

Page 46: Vision and Color Theory

Institute for Visualization and Perception ResearchI VP R 46

© Copyright 1998 Haim Levkowitz

Color vision: Trichromacy ...

• Three broadband filters

• Tuned to three ranges of wavelength

• "Blue," "green," "red"-misnomers! …

• Perceived hue ...

Page 47: Vision and Color Theory

Institute for Visualization and Perception ResearchI VP R 47

© Copyright 1998 Haim Levkowitz

"Blue," "green," "red"-misnomers! …

• Each one color blind …

• "Blue"

• Peaks at "blue" wavelength

• "Green" and "red"

• Largely overlapping

• Peak at roughly "yellow"

Page 48: Vision and Color Theory

Institute for Visualization and Perception ResearchI VP R 48

© Copyright 1998 Haim Levkowitz

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”

Page 49: Vision and Color Theory

Institute for Visualization and Perception ResearchI VP R 49

© Copyright 1998 Haim Levkowitz

Perceived hue …

• Combination across three cone mechanisms …

• Example …

• Metamerism ...

Page 50: Vision and Color Theory

Institute for Visualization and Perception ResearchI VP R 50

© Copyright 1998 Haim Levkowitz

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”

Page 51: Vision and Color Theory

Institute for Visualization and Perception ResearchI VP R 51

© Copyright 1998 Haim Levkowitz

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

Page 52: Vision and Color Theory

Institute for Visualization and Perception ResearchI VP R 52

© Copyright 1998 Haim Levkowitz

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

Page 53: Vision and Color Theory

Institute for Visualization and Perception ResearchI VP R 53

© Copyright 1998 Haim Levkowitz

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 ...

Page 54: Vision and Color Theory

Institute for Visualization and Perception ResearchI VP R 54

© Copyright 1998 Haim Levkowitz

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

Page 55: Vision and Color Theory

Institute for Visualization and Perception ResearchI VP R 55

© Copyright 1998 Haim Levkowitz

Second Stage: Opponent Processes

• Recombine photoreceptor outputs into 3 opponent channels

R – G Y – BW – Bk

“Red” “Green” “Blue”

Y = R + GRG

Page 56: Vision and Color Theory

Institute for Visualization and Perception ResearchI VP R 56

© Copyright 1998 Haim Levkowitz

Color deficiencies

• ~8% males, < 1% females have some genetic color deficiency

• Most common: deuteranomaly …

• Others …

• Color deficiencies and visual displays ...

Page 57: Vision and Color Theory

Institute for Visualization and Perception ResearchI VP R 57

© Copyright 1998 Haim Levkowitz

Most common: deuteranomaly ...

• 5% males, 0.5% females

• Anomalous trichromacy

• Caused by abnormal M-type cone

• ==> abnormal matches/poor discrimination

Page 58: Vision and Color Theory

Institute for Visualization and Perception ResearchI VP R 58

© Copyright 1998 Haim Levkowitz

Others …

• Missing/abnormal cone type …

• Much less common

Page 59: Vision and Color Theory

Institute for Visualization and Perception ResearchI VP R 59

© Copyright 1998 Haim Levkowitz

Missing/abnormal cone type ...

• E.g.,

• Abnormal M-type …

• Abnormal L-type ...

Page 60: Vision and Color Theory

Institute for Visualization and Perception ResearchI VP R 60

© Copyright 1998 Haim Levkowitz

Abnormal M-type ...

Normal

Page 61: Vision and Color Theory

Institute for Visualization and Perception ResearchI VP R 61

© Copyright 1998 Haim Levkowitz

Abnormal L-type ...

Normal

Page 62: Vision and Color Theory

Institute for Visualization and Perception ResearchI VP R 62

© Copyright 1998 Haim Levkowitz

Color deficiencies and visual displays ...

• Color deficient people may not be aware

• Simple rule-of-thumb ...

Page 63: Vision and Color Theory

Institute for Visualization and Perception ResearchI VP R 63

© Copyright 1998 Haim Levkowitz

Simple rule-of-thumb ...• Code important distinctions with

redundancy

• Luminance

• E.g., spell-checker highlights misspelled words

• Red

• Bright

• Size

Page 64: Vision and Color Theory

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 ...

Page 65: Vision and Color Theory

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)

Page 66: Vision and Color Theory

Institute for Visualization and Perception ResearchI VP R 66

© Copyright 1998 Haim Levkowitz

The Luminance Mechanism …

• Mediates high spatial-frequency tasks …

• Has broader bandwidth ...

Page 67: Vision and Color Theory

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

Page 68: Vision and Color Theory

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

Page 69: Vision and Color Theory

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 ...

Page 70: Vision and Color Theory

Institute for Visualization and Perception ResearchI VP R 70

© Copyright 1998 Haim Levkowitz

Small color targets achromatic ...

Page 71: Vision and Color Theory

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 ...

Page 72: Vision and Color Theory

Institute for Visualization and Perception ResearchI VP R 72

© Copyright 1998 Haim Levkowitz

Small "pale salmon" paint chip ...

Page 73: Vision and Color Theory

Institute for Visualization and Perception ResearchI VP R 73

© Copyright 1998 Haim Levkowitz

Looks like "mango frenzy" on a large wall ...

Page 74: Vision and Color Theory

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 ...

Page 75: Vision and Color Theory

Institute for Visualization and Perception ResearchI VP R 75

© Copyright 1998 Haim Levkowitz

Device independent color …

• Definition …

• Steps to device independent color ...

Page 76: Vision and Color Theory

Institute for Visualization and Perception ResearchI VP R 76

© Copyright 1998 Haim Levkowitz

Definition ...

• Color images look comparable

• On CRT

• Projected

• On printer

Page 77: Vision and Color Theory

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

Page 78: Vision and Color Theory

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 ...

Page 79: Vision and Color Theory

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

Page 80: Vision and Color Theory

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

Page 81: Vision and Color Theory

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

Page 82: Vision and Color Theory

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

Page 83: Vision and Color Theory

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)

Page 84: Vision and Color Theory

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

Page 85: Vision and Color Theory

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

Page 86: Vision and Color Theory

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?

Page 87: Vision and Color Theory

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?

Page 88: Vision and Color Theory

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

Page 89: Vision and Color Theory

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 ...

Page 90: Vision and Color Theory

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

Page 91: Vision and Color Theory

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

Page 92: Vision and Color Theory

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 ...

Page 93: Vision and Color Theory

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) ...

Page 94: Vision and Color Theory

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

Page 95: Vision and Color Theory

Institute for Visualization and Perception ResearchI VP R 95

© Copyright 1998 Haim Levkowitz

Most salient "popout" features ...

• Color

• Depth

• [Nakayama, 1986]

Page 96: Vision and Color Theory

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

Page 97: Vision and Color Theory

Institute for Visualization and Perception ResearchI VP R 97

© Copyright 1998 Haim Levkowitz

Preattentive features can distract ...

NYMXOLORSEZPGRDBEINGNP

HZMAYTQCJMAK

MWHAIJSE

Page 98: Vision and Color Theory

Institute for Visualization and Perception ResearchI VP R 98

© Copyright 1998 Haim Levkowitz

Or attract ...

NYMXOLORSEZPGSYBSEINGNP

HZMAYTQCJMAK

MWEAIJSE

Page 99: Vision and Color Theory

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

Page 100: Vision and Color Theory

Institute for Visualization and Perception ResearchI VP R 100

© Copyright 1998 Haim Levkowitz

The Stroop Effect (1935) ...

BLUEGREENREDYELLOWREDYELLOWGREENBLUE

YELLOWBLUEREDGREENREDBLUEYELLOWGREEN

Page 101: Vision and Color Theory

Institute for Visualization and Perception ResearchI VP R 101

© Copyright 1998 Haim Levkowitz

Color contrast and color constancy …

• Color contrast ...

Page 102: Vision and Color Theory

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

Page 103: Vision and Color Theory

Institute for Visualization and Perception ResearchI VP R 103

© Copyright 1998 Haim Levkowitz

Perceived hue depends on surrounding hues ...

Page 104: Vision and Color Theory

Institute for Visualization and Perception ResearchI VP R 104

© Copyright 1998 Haim Levkowitz

Induction of complementary hue ...