Colour

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Colour

• CPSC 533C
• February 3, 2003
• Rod McFarland

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Ware, Chapter 4

• The science of colour vision
• Colour measurement systems and standards
• Opponent process theory
• Applications

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The science of colour vision

• Receptors and trichromacy theory

Red ?
Green ?
Blue ?
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Colour measurement systems and standards

• Any colour can be matched using a combination of
  three primaries.
• The primaries are not necessarily red, green, and
  blue. Any three different colours can be used.
  The range of colours that can be produced from a
  given set of primaries is the gamut.

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Colour standards

• CIE (Commission Internationale dÉclairage)
• Primaries chosen for mathematical properties do
  not actually correspond to colours. These
  virtual colours X, Y, and Z are called
  tristimulus values.
• Y is the same as luminance

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CIE Chromaticity

• Chromaticity is derived from tristimulus values
• Since xyz1, just use x, y values and luminance
  (Y).
• Chromaticity diagram

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Uniform Colour Space - CIEluv

• Uniform colour space a representation where
  equal distances in space correspond to equal
  distances in perception
• Useful for
• Specification of colour tolerances
• Color coding (maximum distinction)
• Pseudocolour sequences to represent ordered data
  values
• CIE XYZ color space is not uniform
• CIEluv is a transformation of the chromaticity
  diagram

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• CIEluv does not solve all problems
• Contrast effects
• Small colour patches difficult to distinguish
  colours in the yellow-blue direction

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Opponent process theory

• Black-white (luminance), red-green, and
  blue-yellow opponents
• Has basis in biology and culture
• Should use opponent colours for coding data

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Properties of Colour Channels

• Isoluminant / Equiluminous patterns a colour
  pattern whose components do not differ in
  luminance
• Red-green and yellow-blue channels carry only
  about 1/3 of the detail carried by black-white.

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Yellow Text on a Blue Background

• Is fairly easy to read unless the text is
  isoluminant with the background colour. As the
  luminance of the background becomes the same as
  the luminance of the text, it is very difficult
  to make out what the text says. So much so, that
  at this point I can write just about anything I
  want here and hardly anyone would want to put in
  the effort to see what it was I had written.

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Other isoluminance effects

• Stereoscopic depth is not detectable with
  isoluminant colours
• Isoluminance in animation makes it appear to be
  slower than the same animation in black-and-white
• Shape and form are best shown using luminance

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Colour appearance

• Contrast
• Saturation
• Brown

low
high
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Applications

• Colour selection interfaces
• Colour naming
• Natural Colour System (NCS) e.g. 0030-G80Y20
• Blackness 00, intensity 30, green 80, yellow 20
• Pantone, Munsell standard colour chips

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Applications

• Colour for labelling (nominal information
  encoding)
• Distinctness
• A rapidly distinguished colour lies outside the
  convex polygon defined by the other colours in
  CIE space

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Applications

• Colour for labelling (2)
• Unique hues universally recognized hues (red,
  green, blue, yellow, black, white) should be used
  
• Contrast with background border around objects

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Applications

• Colour for labelling (3)
• Colour blindness majority of colour-blind people
  cannot distinguish red-green, but most people can
  distinguish blue-yellow
• Number only 5-10 codes easily distinguished

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Applications

• Colour for labelling (4)
• Size
• Colour-coded objects should not be very small
  (about ½ degree minimum size).
• Smaller objects should be more highly saturated,
  large colour-coded regions should have low
  saturation. Text highlighting should be
  high-luminance, low-saturation.
• Conventions
• Common usage of colours, e.g. redstop,
  greenready, bluecold

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Applications

• Colour for labelling (5)
• Wares 12 recommended colours (in order of
  preference)

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Applications

• Pseudocolour sequences for mapping
• Pseudocolouring is the practice of assigning
  colour to map values that do not represent colour
• Medical imaging
• Astronomical images
• Mapping nonvisible spectrum information to the
  visible spectrum (astronomy, infrared images)
• Gray scale best for showing surface shape
• Colour best for classification

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Applications

• Colour for mapping (2)
• For orderable sequences, black-white, red-green,
  blue-yellow, or saturation (dull-vivid) sequence
  can be used.
• For detailed data, the sequence should be based
  mainly on luminance. For low letail, chromatic or
  saturation sequences can be used.
• Uniform colour spaces can be used to create
  colour sequences where equal perceptual steps
  correspond to equal metric steps.
• Where it is important to be able to read off
  values from a colour map, a sequence that cycles
  through many colours is preferable.

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Applications

• Colour for mapping (3)
• A spiral through colour space (cycling through
  several colours while continuously increasing in
  luminance) is often a good choice.
• Hue 0, 50,250, 45, 95
• Luminance 0, 25, 50 225

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Applications

• Colour for mapping (4)
• Perception even if the sequence is smooth,
  people tend to see discrete colours, potentially
  miscategorizing data.
• My personal division into blue, green, yellow,
  orange, red, purple very nonlinear

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Applications

• Colour for mapping (5)
• Using colour for 3-D information mapping
• Difficult to read accurately
• May be used to identify regions
• Satellite images regions of invisible spectrum
  mapped to red, green, blue channels

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Applications

• Colour for multidimensional discrete data
• 5-D plot using (x, y) position, red, green, blue
• Possible to identify clusters
• Ambiguous is a point low-red or high-green?
• Other methods needed to analyze clusters once
  identified

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Rogowitz et al. How Not to Lie with Visualization

• Visual representation of data affects the
  perceived structure of the data.

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Enhancing data interpretation using Colour

• Perceptual impact of a colour is not predictable
  from the red/green/blue components of the colour
• Mapping different aspects of colour to different
  data is not intuitively decodable by users.
• Default colour maps rainbow
• Perceptual nonlinearity
• False contours
• Yellow attracts attention

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Guiding colour map selection

• Constrain the set of colour maps available to the
  user based on
• Data type
• Data spatial frequency
• Visualization task
• Other design choices made by user

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Representing Structure

• Nominal data
• Object should be distinguishably different but
  not perceptually ordered
• Ordinal data
• Distinguishable with perceptual ordering
• Interval data
• Equal steps in data correspond to equal steps in
  perceived magnitude
• Ratio data
• Zero point distinguishable in colour sequence

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Structure

• Magnitude of a variable at every spatial position
• Use luminance (gray scale) or saturation

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Spatial Frequency
high spatial frequency
low
saturation-based
luminance-based
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Segmentation

• Low frequency more segmentation steps can be
  used

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Highlighting
Luminance-based map can be highlighted using hue
variations. The highlighted regions have the same
luminance value as the rest of the map.
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PRAVDA

• Perceptual Rule-Based Architecture for
  Visualizing Data Accurately
• Part of IBMs Visualization Data Explorer
  (http//www.research.ibm.com/dx/)
• Provides choices for colour maps based on spatial
  frequency, data type, and user-selected goal
  isomorphic (structure-preserving), segmentation,
  highlighting

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PRAVDA
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