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Intro to Color for Information Display
Color Theory Color Geometries Color applications
Electronic Chart Example
Spectrum approximation, visual segmentation
Double-Ended Income
Olson ‘97, fig. 11-8.
Hue Saturation Bivariate.
Tufte ‘83, pg. 153.
Bivariate map
Ware:Vislab:CCOM
Low errors but big patches (only 13)These kinds of color sequences are known to give very large errors
Attribute Blocks (Miller, 2007)
Color weaving (Hagh-Shenas et al, 2007)
Temperature and Pressure
Physiology: Receptors
Rods active at low light levels (night vision) only one wavelength sensitivity function 100 million rod receptors
Cones active at normal light levels three types: sensitivity functions peaks at different
wavelengths (“red”, “green”, “blue”) 6 million cone receptors Focused in the center of vision (fovea)
The basis of color visionand measurementCone Sensitivity Functions
Color Measurement
a
G+B +R
a b
A three primary system
Consider three lamps
Color measurement
Based on the “standard observer”
CIE tristimulus values XYZ
Y is luminance. Assumes all humans
are the same
CIEluv
x = X/(X+Y+Z), y = Y/(X+Y+Z)
Terms
Luminance (physical) Brightness (perceived amount of light) Lightness (perceive reflectance) Value – used in Computer graphics
Hue (color) Saturation (strength or vividness)
Luminance
Is a physical measurement of the amount of light in the environment weighted by the sensitivity to flickering light.
L = VEd
Brightness: amount of perceived light Brightness (in the dark) =L 1/3
Gamma and Gamma Correction Lightness: perceived surface reflectance
400
700
Eye sensitive over 9 orders or magnitude 5 orders of magnitude (room – sunlight) Receptors bleach and become less
sensitive with more light Takes up to half an hour to recover
sensitivity We are not light meters
Luminance is not Brightness
Short wavelength sensitive cones
Blue text on a dark backgroundis to be avoided. We have very fewshort-wavelength sensitive cones in the retina and they are not very sensitive
Blue text on dark backgroundis to be avoided. We have very fewshort-wavelength sensitive cones in the retina and they are not very sensitive
Blue text on a dark backgroundis to be avoided. We have very fewshort-wavelength sensitive cones in the retina and they are not very sensitive.Chromatic aberration in the eye is also a problem
Blue text on a dark backgroundis to be avoided. We have very fewshort-wavelength sensitive cones in the retina and they are not very sensitive
Opponent Process Theory Cone signals transformed into new
channels
Color Channel Theory
Luminance contrast needed to see detail
3:1 recommended10:1 idea for small text
Comparing the Channels Spatial Sensitivity
Red/Green and Yellow/Blue each about 1/3 detail of Black/White
Stereoscopic Depth Pretty much can’t do it with hue alone
Temporal Sensitivity Moving hue-change patterns seem to move slowly
Form Shape-from shading works well Shape-from-hue doesn’t
Information Labeling: Hue works well!
Some natural philosophersSuppose that these colors arise from the accidental vapours diffused in the air, which communicates their own hues to the shadow
Some natural philosophersSuppose that these colors arise from the accidental vapours diffused in the air, which communicates their own hues to the shadow
Some natural philosophersSuppose that these colors arise from the accidental vapours diffused in the air, which communicates their own hues to the shadow
Luminance contrast
Lightness constancy
Visual system extracts surface information Discounts illumination level Discounts color of illumination Mechanisms 1) Adaptation 2) Simultaneous contrast
Contrast for constancy
Contrast for constancy
Brightness Lightness and Luminance
Luminance – a physical measurement
Brightness refers to perception of lights
Brightness non linear Monitor Gamma: L = V
Lightness refers to perception of surfaces
Perceived lightness depends on a reference white
Gamma = approx 2.0
Luminance for Shape-from-shading
Playing with the background
Miscellaneous of contrast effects
SharpeningNon-linear grey scalesLuminance contrast required for detail
Chromatic Color is Irrelevant…
To perceiving object shapes To perceiving layout of objects in space To perceiving how objects are moving Therefore, to much of modern life
Laboratory assistant went 21 years without realizing he was color-blind
Color is Critical…
To help us break camouflage To judge the condition of objects (food) To determine material types Extremely useful for coding information
Other contrast effects
Small field tritanopiaChromatic contrast
Color “blindness”
A 3D to a 2D space 8 % of males R-G color blindness
Can generate color blind acceptable palette
Yellow blue variation OK
Implications
Color perception is relative We are sensitive to small differences-
hence need sixteen million colors Not sensitive to absolute values- hence
we can only use < 10 colors for coding
Channel Properties
Luminance Channel Detail Form Shading Motion Stereo
Chromatic Channels Surfaces of things Labels Berlin and Kay Categories (about 6-
10) Red, green, yellow
and blue are special (unique hues)
Applications
Color interfaces Color coding Color sequences Color for multi-dimensional discrete data
Color Planes
Saturation
Hue
Data: code objectscode maps
Measurement Scales (Stevens)
Nominal (labeling) Ordinal (orderable) Interval (meaningful step sizes) Ratio (Zero, ratios)
Color great for classification
Rapid visual segmentation
Color helps us determine type
Only about six categories
a
whiteblack
green yellow
green
blue brown
pinkpurpleorangegrey
red
yellow
Color Categories
Task: Name the colors Regions same > 75% Nonuniform sizes Only 8 hues named
small number of labels
Color Coding
Large areas: low saturation
Small areas high saturation
Break isoluminance with borders
Must have luminance contrast with background to see details
Which colors
-Distinct and-memorable
Cultural semantics
The fact that certain colors are special is because they are hard wired
The meaning of those colors is culturally determined
Electronic Chart Example
Color Coding
The same rules apply to color coding text and other similar information. Small areas should have high saturation colors.
Large areas should be coded with low saturation colors
Luminance contrast should be maintained
Color examples
Ordinal DataPseudo-color sequences
One approach: Change a single color model component with other components held constant
Examples Grey scale Saturation scale Spectrum (hue, rainbow) scale (not good)
Issues: How can we see forms (quality) How we read value (quantity)
Luminance Vs Spectrum
Spectrum not a Perceptual
Sequence. Visually Segments
Luminance good for form
High contrast errors
Pseudo-color sequences
Spectrum sequence
Luminance (Gray) Scale
Saturation and Luminance Scale
Spectrum approximation, visual segmentation
Hue+Lightness - Highlighting
Ordinal data Principles
Order: ordered values should be represented by perceptually-ordered colors
Separation: significantly different levels should be represented by distinguishable colors
Can use uniform color spaces
Luminance: good for showing form Many hues: useful for showing readable values
Redundant Color Scales
Two or more color components varied together
Examples Hue with lightness
Characteristics Reinforces signal Combines characteristics of simpler scales
Hue+Lightness Spiral
Spiral Sequence
Interval sequences: Contour Lines and Color
Both indicate regions Contour by showing the boundaries Bands by showing the interiors
Choice of spacing Regular intervals to enable interval comparison Specific values to highlight regions (sea level) Choice of spacing
Can attempt to use UCS
Interval – contours and color
Interval – step color Note spectrum sequence
Uncertainty
Ratio sequence to represent zeroDouble-ended Scale
Represent neutral by neutral on opponent-color channels
Ordered (and double-ended)
Tufte ‘97, pg. 76.
Double-Ended Income
Olson ‘97, fig. 11-8.
Neutral = 0
Bivariate Maps Rows and columns: to preserve univariate
information, display parameters should should be perceptually orthogonal (ideally)
Hue Saturation Bivariate.
Tufte ‘83, pg. 153.
Bivariate map
Bivariate MapValue 1 Luminance – shadingValue 2 Surface hue
Consider Data
Nominal, ordinal interval or ratio Map or point data coding Amount of detail High spatial frequency?
Vary lightness in addition to hue
Consider Audience
Color deficient viewers? Don’t depend on red-green differentiation Use redundant scales
Application area conventions? Use familiar scales (or at least know when you’re not)
Color associations with variables? Use associated color
Color associations with data ranges? Use red for bad range (in U.S.) Use red for hot
Pseudo-color sequences
Issues: How can we see forms (quality) How we read value (quantity)
Multi - Dimensional Scatterplot
V1 - xposition V2 - yposition V3 - red V4 - green V5 - blue Can identify clusters and trends Cannot read values back from visualization
Show grouping
Ware and Beatty ‘85, p. 22
Scatterplot
Scatterplot Results
For cluster discrimination Color adds as much as space
Except for certain special conditions where one cluster lies above another.
3
Take home messages
Use luminance for detail, shape and form Use color for coding - few colors Minimize contrast effects Strong colors for small areas - contrast in
luminance with background Subtle colors can be used to segment
large areas
More messages
Color excellent for multi-dimensional data Use additional tools to get quantities Beware of artifacts due to color re-
sampling We need precise color for specification
and standardization
Luminance to signal direction
Bivariate Map