Chapter 13: Color Processing
。 Color: An important descriptor of the world
。 The world is itself colorless
。 Color is caused by the vision system
responding differently to different
wavelengths of light.
13-1
。 Image color depends on:
(1) The color of the incidence light
(2) The color of the scene surface
(3) The nature of the visual sensor
13-2
○ The Human Eye
13-3
Two kinds of
photoreceptors:
rods, cones
13-4
Rods -- sensitive to light
Cones -- sensitive to color
Three types of cones:
S , M , L ( R, G , B )
13-5
○ RGB Color Space -- Many colors are made
up of varying amounts of red, green and
blue C ( )  w1 ( ) R  w2 ( )G  w3 ( ) B
R, G, B: primary colors, real
w1 , w2 , w3 : color matching functions
may be negative
13-6
○ CIE XYZ Color Space
CIE (Commission Internationale d’Eclairage):
an organization responsible for color standard
X,Y,Z: not real primaries, Y: luminance
Their color matching functions are positive
everywhere
。 The volume of visible
colors in CIE XYZ
space is a cone
13-7
。The relationship between RGB and XYZ
 X   0.431 0.342 0.178   R 
 Y    0.222 0.707 0.071 G 
  
 
 Z   0.02 0.130 0.939   B 
 R   3.063 1.393 0.476   X 
G    0.969 1.876
0.042   Y 
  
 
 B   0.068 0.229 1.069   Z 
13-8
○ CIE xy Color Space -- A constant brightness
section intersects the XYZ space with the
plane X  Y  Z  1
X
Y
Z
x
, y
, z
X Y  Z
X Y  Z
X Y  Z
Since x + y + z = 1, a color can be specified
by x and y alone.
13-9
。 Chromaticity Diagram
(i) Spectral locus: the
curved boundary
along which colors
of single wavelengths
are viewed
(ii) Neutral point: the
color whose weights
are equal for all
three primaries
(iii) Colors that lie farther away from the
neutral point are more saturated
13-10
。RGB Gamut – The colors correspond to
positive matching values
13-11
。Secondary colors (primaries of pigments):
Magenta (purple) = R + B = W - G
Cyan = G + B = W - R
Yellow = R + G = W - B
。 Pigments remove color from incident light,
which is reflected from paper
e.g., Red ink absorbs green and blue light;
incident red light passes through the
ink and is reflected from the paper
13-12
○ HSV (Hue, Saturation, Value) Color Space
Hue: varies from red  green
Saturation: varies from red  pink
Value: varies from black  white
13-13
○ (i) RGB
HSV
V  max{R, G, B},
  V  min{R, G, B}, S   / V
1G B 
If V = R, then H  

6  
1
BR
If V = G, then H   2 

6
 
If V = B, then H  1  4  R  G 
6
 
If H ends up being negative, add 1.
If (R,G,B) = (0,0,0), then (H,S,V) = (0,0,0)
13-14
。 Example: (R, G, B) = (0.2, 0.4, 0.6)
V  max{R, G, B}  max{0.2,0.4,0.6}  0.6
  V  min{R, G, B}  0.6  min{0.2,0.4,0.6}  0.4
S   / V  0.4 / 0.6  0.6667
Since B  V  0.6,
1
R  G  1  0.2  0.4 
H  4
  4
  0.5833
6
  6
0.4 
13-15
(ii) HSV
RGB
H   6 H 
H R
G
B
0 V
T
P
F  6H  H 
1
Q V
P
P  V (1  S )
2
P V
T
Q  V (1  SF )
3
P Q V
T  V [1  S (1  F )]
4
T
P V
5 V
P Q
13-16
。 Example: (H, S, V) = (0.5833, 0.6667, 0.6)
H   6 H    6(0.5833)   3
F  6 H  H   6(0.5833)  3  0.5
P  V (1  S )  0.6(1  0.6667)  0.2
Q  V (1  SF )  0.6(1  0.6667  0.5)  0.4
T  V [1  S (1  F )]  0.6[1  0.6667 
(1  0.5)]  0.4
Since H   3,
( R, G, B)  ( P, Q,V )  (0.2, 0.4, 0.6)
13-17
○ YIQ Color Space – Used for TV and video
Y : luminance information
I, Q : color information
Y   0.299 0.587 0.114   R 
 I    0.596 0.274 0.322  G 
  
 
Q   0.211 0.523 0.312   B 
0.621   Y 
 R  1.0 0.956
G   1.0 0.272 0.647   I 
  
 
 B  1.0 1.106 1.703  Q 
13-18
○ Uniform Color Space
-- The distance in the space is a guide to
color difference
。 Noticeable difference – the difference when
modifying a color until one can tell it has
changed
。 Macadam ellipse -- the noticeable difference
of a color forms the boundary of the color in
a color space and can be fitted with an ellipse
13-19
The color difference in CIE xy space is poor:
(a) the ellipses at the top are larger than
those at the bottom
(b) the ellipses rotate as they move up
13-20
。 CIE u’v’ Color Space – a more uniform
space than the CIE xy color space
4X
9Y
(u, v)  (
,
)
X  15Y  3Z X  15Y  3Z
13-21
○ CIE Lab Color Space
– another substantial uniform space
Y 1/ 3
*
L  116( )  16
Yn
X 1/ 3 Y 1/ 3
a  500[( )  ( ) ]
Xn
Yn
*
Y 1/ 3
Z 1/ 3
b  200[( )  ( ) ]
Yn
Zn
*
where X n , Yn , Z n : the XYZ coordinates of
a reference white patch
13-22
◎ Color Images
13-23
13-24
◎ Pseucoloring
。 Intensity Slicing
13-25
。Transformation
Define colormap functions:
f R ( x), fG ( x), f B ( x), x : gray level
(R, G, B) = 255 x
(0.125, 0.375, 0.75)
= (32, 96, 128)
13-26
◎ Processing of Color Images
Two methods:
(a)
(b)
13-27
○ Noise Reduction
R
Apply
median
filter to
R,G,B
G
B
Apply
median
filter to
Y
13-28
○ Contrast Enhancement
Perform on the intensity component
(1) RGB  YIQ
(2) Apply histogram equalization to
Y  Y’
(3) Y’IQ  R’G’B’
13-29
○ Spatial Filtering
Both low- and high- pass filters are better
of applying to the intensity component
13-30
○ Edge Detection
Two ways:
(1) Apply edge detection to the intensity
component
(2) Apply edge detection to each RGB
component
13-31