Chapter 2: Color Basics

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Chapter 2 Color Basics
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What is light?

• EM wave, radiation
• Visible light has a spectrum wavelength from 400
  780 nm.
• Light can be composed of radiation of various
  wavelengths.
• Light can be described by a spectral power
  distribution (SPD).

power
wavelength
B
G
R
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Spectrum
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SPD
Taken from Adobe
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What is color?

• Human retina has
• 3 types of color photoreceptor cone cells
• rod cells (only effective at extremely low light
  levels, or night-vision).
• When light falls on the retina, the 3 types of
  cone cells respond (at different levels) to
  create the sensation of color.
• CIE (Commission Internationale de LEclairage)
  specifies how an SPD can be transformed into
  tri-stimulus values (XYZ) that specify a color.

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Sensitivity
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For your interest

• Mantis shrimps have 16 photoreceptors, about 4 of
  them are in the ultraviolet region
• While human beings can distinguish some 10,000
  colors, mantis shrimps can distinguish 110,000
  colors.

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What is color?

• Given 3 primary lights red, green, and blue,
  most colors can be described by a triplet RGB.
  Each value is (linearly) proportional to the
  physical power (radiance) of a component light.
• We call these linear RGB values.

R0.81 G0.52 B0.56
R0.36 G0.69 B0.74
R0.0 G0.0 B0.69
R0.0 G0.0 B0.91
R0.91 G0.91 B0.91
R0.69 G0.69 B0.69
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Gamma

• A color CRT has 3 types of phosphors that emit
  different-colored (RGB) light.
• Intensity measures the power of a radiating light
  source.
• A typical CRT has a non-linear transfer function.
  That is, the amount of power (intensity) emitted
  is approximately proportional to the applied
  voltage (scaled to the range 0,1) raised to a
  constant power, commonly called gamma, ?.
• Intensity ? Voltage?
• Typical CRTs have gamma values close to 2.5.

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Gamma
intensity
1
gamma lt 1
gamma 1
gamma gt 1
voltage
0
1
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Gamma
intensity
1
gamma 1
gamma gt 1
0.176
voltage
0
0.5
1
12
Gamma
intensity
1
0.77
gamma 1
gamma gt 1
0.031
voltage
0
1
0.9
0.25
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Gamma correction

• A camera records the intensity (i.e., radiance)
  of an object. If the recorded values are
  translated into a voltage signal, and applied
  directly to a CRT, the reproduced image will have
  a distorted intensity.
• A typical camera, therefore, applies a gamma
  correction transfer function. That is, it applies
  a 1/gamma power function to the recorded RGB
  values
• R R1/gamma (likewise for G and B)
• Note the RGB values used in the formula are all
  normalized to the 0,1 range.

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Gamma correction
1.0
0.5
1.0
0.176
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Gamma (example)
as displayed on a CRT without gamma correction
original
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Gamma correction

• The gamma corrected values RGB are
  distinguished from the original RGB values by the
  prime notation. They are called non-linear (or
  gamma corrected) RGB.
• When we are processing digital images, the pixel
  values we are dealing with are usually the
  non-linear (RGB) values.

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Gamma correction
eye
RGB
RGB
camera
gamma correction
computer
RGB
CRT
RGB
x
x1/?
(x1/?) ?
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Lightness

• Human vision system has a non-linear perceptual
  response to intensity
• e.g., a source having a luminance (power) of 18
  of another source appears about half as bright.
• The perceptual response to intensity is called
  lightness.
• The transfer function of human perceptual
  response to intensity resembles (very roughly) a
  0.4 power curve, i.e.,
• Lightness ? Intensity0.4

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Lightness
RGB
computer
CRT
RGB
y
y?
(y?)0.4
lightness
intensity
pixel value
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Lightness

• 0.4 2.5 1.0. Hence, amazingly, the human
  transfer function is roughly the inverse of that
  of a CRT.
• Recall that the nonlinear RGB values are
  (usually) used in a computer as pixel values.
• The nonlinear values are used to drive a CRTs
  voltage for controlling the brightness of
  phosphors.
• The intensity emitted by a CRT is a 2.5 power
  curve.
• The human vision system has a transfer function
  of a 0.4 power curve.
• Hence, the nonlinear RGB values are
  approximately proportional to lightness (a
  perceptual value).

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Luminance efficiency

• If one looks at 3 light sources red, green, and
  blue, each at the same power, then the green
  source appears the brightest, followed by the red
  source and then by the blue source.
• Human vision system is more sensitive to
  luminance than to color. A video system usually
  encodes RGB as 1 luminance component Y (or luma)
  and 2 chrominance components (or chroma).
• The chrominance components are usually given
  lower bandwidth (or data capacity) than the
  luminance component.

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Spectral sensitivity
TakenfromAdobe
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Cone cell distribution
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Color differences

• Luma (Y) can be computed as a weighted sum of
  non-linear RGB components
• ITU-R Recommendation BT. 601, formerly known as
  CCIR 601
• Y 0.299R 0.587G 0.114B
• Color differences refer to color components where
  (informally) brightness is removed.
• The standard is to subtract luma from non-linear
  blue and from non-linear red
• B-Y and R-Y (these are called chroma).

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Scaling chroma

• Various scaling factors are applied to the chroma
  values (B-Y, R-Y) for different applications
• YPBPR for component analog video
• YUV for composite analog video (PAL)
• YIQ ditto (NTSC)
• YCBCR for digital images and video

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Chromatic sub-sampling

• The CB, CR chroma components are usually
  sub-sampled to reduce the data requirement in
  digital images and video.
• 422 sub-sample chroma horizontally by a factor
  of 2 (Rec. 601).

a row of pixels
take these chroma values
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Perceptual uniformity

• Human vision responds to about a hundred-to-one
  contrast ratio. That is, if the difference in
  intensity of two light sources is less than 1,
  our eyes wont detect the difference.

Can you see the circle?
decreasing ?I
I?I
I
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Contrast ratio

• Contrast ratio refers to the ratio of intensity
  between the brightest spot and that of the
  darkest spot of a particular display device and
  environment.
• Example 301 (TV, home with mild lighting)

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Why 8 bits per sample

• Let L be the intensity level of the darkest spot.
  With a contrast ratio of 20, the brightest spot
  has an intensity of 20L.
• We need
• 1 code to represent dark (i.e., L)
• 1 code to represent 1.01 dark (i.e., 1.01L)
• 1 code to represent (1.01)2 L
• 1 code to represent 20L
• Hence, we need about 300 codes (or quantization
  levels)
• 8 bits are used because they can be conveniently
  packed into a byte.

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Why 8 bits per sample
intensity
30L
(1.01)3L
Intensities within this region areindistinguishab
le from 1.01L
(1.01)2L
1.01L
Intensities within this region areindistinguishab
le from L
L
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Perceptual uniformity
00000000 00000001 . 11111111
256codes

• If we use an 8-bit linear coding system to
  represent luminance (i.e., intensity), we may not
  be making good use of the 256 codes.

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100
200
Y
0
255
26
101
201
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Perceptual uniformity

• If we use an 8-bit linear coding system to
  represent luminance (i.e., intensity), we may not
  be making good use of the 256 codes.

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100
200
Y
0
255
26
101
201
?
?
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Contouring

• Luminance codes above 100 suffer no artifacts due
  to visibility of the jumps between codes. Some
  codes are not useful.
• Also, successive codes that are near black has
  poor luminance resolution. This leads to
  contouring.

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Contouring
A
25
B
26
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Intensity
Intensity
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27
26
26
25
25
B
B
A
A
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Contouring
5 bits 32 gray levels
8 bits 256 gray levels
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Perceptual uniformity

• If we code the non-linear gamma corrected value
  (i.e., luma) instead, contouring is ameliorated.

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100
200
0
255
26
101
201
0.098
0.391
0.781
Y
0
1
0.102
0.395
0.785
0.539
0.394
0.687
0.906
0
1
Y
0.401
0.689
0.908
101
232
175
0
255
103
176
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Gamma Correction

• Gamma correction thus helps
• to compensate the non-linearity of a typical CRT
• to allow a more perceptually uniform coding.

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True color

• A true color system (or a 24-bit system)
  represents a pixel by its RGB (or RGB) values
  with 8 bits for each component.
• A 24-bit system allows us to describe 224
  16,777,216 different colors. In many cases, such
  a high color resolution is not needed.
• Example
• How many different colors can you see in
• a 640 ? 480 image?
• your window desktop?

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Pseudocolor/indexed color/ colormapped system

• To save storage space, we can analyze an image to
  find out all the distinct colors that are present
  in the image.
• Let n be the number of distinct colors found
  (usually n ltlt 16.7M). We assign a code to each
  distinct color.
• We keep a Color Lookup Table (CLUT, or colormap,
  or palette) that translates a code to its
  corresponding RGB values.
• Each color is thus represented by ?log2n? bits.

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CLUT

• Using the CLUT approach,
• We waste space for storing the CLUT.
• We save space by using fewer bits for each pixel.
• Usually the latter factor outweighs the first.

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CLUT
640 x 238
How much storage space is neededfor a true color
system? For a CLUT system?
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