Introduction to Color
Spaces
Author: Chik-Yau Foo
E-mail: r89922082@ms89.ntu.edu.tw
Mobile phone: 0920-767-580
v030305
Presenter: Wei-Cheng Lin
E-mail: r97944028@ntu.edu.tw
Mobile Phone: 0912-808-362
1
106
Long-wave radio
103
Short-wave radio
109
Microwave
TV
100
Wavelength (m)
 Only a small part of the EM*
spectrum is visible to us.
 This part is known as the
visible spectrum.
 Wavelength in the region
of 380 nm to 750 nm.
Frequency (Hz)
The EM Spectrum
10-3
1012
Infrared
1015
1018
Visible spectrum
Ultraviolet
X-rays
Gamma rays
1021
*Electro-Magnetic
10-6
10-9
10-12
Cosmic rays
2
Light and the Human Eye
 When we focus on an image, light from the image
enters the eye through the cornea and the pupil.
 The light is focused by the lens onto the retina.
Fovea
Lens
Retina
Pupil
Cornea
Optic
nerve
Iris
3
Rods and Cones
 When light reaches the retina,
one of two kinds of light sensitive
cells are activated.
 These cells, called rods and
cones, translate the image into
electrical signals.
Rod
Cone
Retina
light
 The electrical signals are
transmitted through the optical
nerve, and to the brain, where we
will perceive the image.
4
Rods: Twilight Vision
 130 million rod cells per eye.
 Most to green light (about
550-555 nm), but with a
broad range of response
throughout the visible
spectrum.
 Produces relatively blurred
images, and in shades of
gray.
Relative response
 1000 times more sensitive to
light than cone cells.
1.00
0.75
0.50
0.25
0.00
400
500
600
Wavelength (nm)
700
Relative neural response of rods as
a function of light wavelength.
 Pure rod vision is also called
twilight vision.
5
Cones: Color Vision
 7 million cone cells per eye.
 S : 430 nm (blue)
(2%)
 M: 535 nm (green)
(33%)
 L : 590 nm (red)
(65%)
 Produces sharp, color images.
 Pure cone vision is called photopic
or color vision.
S
Relative absorbtion
 Three types of cones* (S, M, L),
each "tuned" to different maximum
responses at:-
1.00
M
L
0.75
0.50
0.25
0.00
400
500
600
Wavelength (nm)
700
Spectral absorption of light by
the three cone types
*S = Short wavelength cone
M = Medium wavelength cone
L = Long wavelength cone
6
Photopic vs Twilight Vision
 There are about 20x more rods than cones in the
eyes, but rod vision is poorer than cone vision.
Rod vision
Cone vision
 This is because rods are distributed all over the
retina, while cones are concentrated in the fovea.
Rod vision
Cone vision
130 million rods
7 million cones
7
Eye Color Sensitivity
1.00
1.00
Relative sensitivity
Relative absorbtion
 Although cone response
is similar for the L, M, and
S cones, the number of the
different types of cones
vary.
 L:M:S = 40:20:1
 Cone responses typically
overlap for any given
stimulus, especially for the
M-L cones.
 The human eye is most
sensitive to green light.
0.75
0.1
S
M
M
L
L
S
0.50
0.01
0.25
0.001
0.00
0.0001400
400
500
600
500
600
Wavelength (nm)
Wavelength (nm)
700
700
Spectral
by
Effectiveabsorption
sensitivityof
of light
cones
the three
(logcone
plot) types
S, M, and L cone distribution in the fovea
8
Theory of Trichromatic Vision
 The principle that the color
you see depends on signals
from the three types of cones
(L, M, S).
 The principle that visible color
can be mapped in terms of the
three colors (R, G, B) is called
trichromacy.
 The three numbers used to
represent the different
intensities of red, green, and
blue needed are called
tristimulus values.
=
r
g
b
Tristimulus values
9
Seeing Colors
 The colors we perceive
depends on:-
Illumination
source
x
Illumination source
Object
reflectance
factor
Object reflectance
x
Observer response
 The product of these three
factors will produce the
sensation of color.
Observer
spectral
sensitivity
=
r
g
Tristimulus values
(Viewer response)
b
Observer
response
10
Additive Colors
 Start with Black – absence of any
colors. The more colors added,
the brighter it gets.
 Color formation by the addition of
Red, Green, and Blue, the three
primary colors
 Examples of additive color usage: Human eye
 Lighting
 Color monitors
 Color video cameras
Additive color wheel
11
Subtractive Colors
 Starts with a white background
(usually paper).
 Use Cyan, Magenta, and/or
Yellow dyes to subtract from
light reflected by paper, to
produce all colors.
 Examples of Subtractive color
use: Color printers
 Paints
Subtractive color wheel
12
Using Subtractive Colors on
Film
 Color absorbing pigments are layered on each other.
 As white light passes through each layer, different
wavelengths are absorbed.
 The resulting color is produced by subtracting
unwanted colors from white.
W
M
B
C
K
G
R
Y
White
light
Green
Pigment layers
Red
Blue
Cyan
Yellow
Magenta
Yellow
Magenta
Cyan
Black
White
Black
Reflecting layer
(white paper)
13
Color Matching Experiment
1. Observer views a split screen of
pure white (100% reflectance).
2. On one half, a test lamp casts a
pure spectral color on the screen.
3. On the other, three lamps
emitting variable amounts of red,
green, and blue light are
adjusted to match the color of
the test light.
4. The amounts of red, green and
blue light used to match the pure
colors were recorded when an
identical match was obtained.
5. The RGB tristimulus values for
each distinct color was obtained
this way.
Color matching experimental setup
Test Light
Primary
Mixture
Tristimulus values
14
Metamerism
 Spectrally different lights
that simulate cones
identically appear identical.
 This phenomena is called
metamerism.
 Almost all the colors that
we see on computer
monitors are metamers.
9
Relative power
 Such colors are called
color metamers.
0
380
480
580
680
Wavelength (nm)
780
The dashed line represents daylight reflected from sunflower, while
the solid line represents the light emitted from the color monitor
adjusted to match the color of the sunflower.
15
 Under trichromacy, any color
stimulus can be matched by a
mixture of three primary stimuli.
Relative power
The Mechanics of Metamerism
0
380
G
780
B
780
380
380
380
S   r    d 
S  g   d
S   b    d 
9
0
380
Relative power
R
480 580 680 780
Wavelength (nm)
Relative power
 Metamers are colors having the
same tristimulus values R, G, and
B; they will match color stimulus
C and will appear to be the same
color.
780
9
480 580 680 780
Wavelength (nm)
9
0
380
480 580 680 780
Wavelength (nm)
The two metamers look the same because
they have similar tristimulus values.
16
Human vision
gamut
Gamut
 A gamut is the range of
colors that a device can
render, or detect.
Photographi
c film gamut
0.8
0.6
 The larger the gamut,
the more colors can be
rendered or detected.
 A large gamut implies a
large color space.
y
0.4
0.2
Monitor
gamut
0
0
0.2
0.4
x
0.6
0.8
17
Color Spaces
 A Color Space is a method by which colors are
specified, created, and visualized.
 Colors are usually specified by using three attributes,
or coordinates, which represent its position within a
specific color space.
 These coordinates do not tell us what the color looks
like, only where it is located within a particular color
space.
 Color models are 3D coordinate systems, and a
subspace within that system, where each color is
represented by a single point.
18
Color Spaces
 Color Spaces are often geared towards specific
applications or hardware.
 Several types:HSI (Hue, Saturation, Intensity) based
RGB (Red, Green, Blue) based
CMY(K) (Cyan, Magenta, Yellow, Black) based
CIE based
Luminance - Chrominance based
CIE: International Commission on Illumination
19
RGB*
 One of the simplest color
models. Cartesian coordinates
for each color; an axis is each
assigned to the three primary
colors red (R), green (G), and
blue (B).
Cyan
(0,1,1)
Blue
(0,0,1)
Magenta
(1,0,1)
White
(1,1,1)
Green
(0,1,0)
Black
(0,0,0)
 Corresponds to the principles of
additive colors.
Red
(1,0,0)
 Other colors are represented as
an additive mix of R, G, and B.
Yellow
(1,1,0)
RGB Color Space
 Ideal for use in computers.
*Red, Green, and Blue
20
RGB Image Data
Full Color Image
Red Channel
Green Channel
Blue Channel
21
CMY(K)*
 Main color model used in the
printing industry. Related to RGB.
White
 Corresponds to the principle of
subtractive colors, using the
three secondary colors Cyan,
Magenta, and Yellow.
Magenta
Red
Blue
Black
 Theoretically, a uniform mix of
cyan, magenta, and yellow
produces black (center of
picture). In practice, the result is
usually a dirty brown-gray tone.
So black is often used as a fourth
color.
*Cyan, Magenta, Yellow, (and blacK)
Cyan
Green
Yellow
Producing other colors from subtractive colors.
22
CMY Image Data
Full Color Image
Cyan Image (1-R)
Magenta Image (1-G)
Yellow Image (1-B)
23
CMY – RBG Transformation
 The following matrices will perform transformations between
RGB and CMY color spaces.
 Note that: R = Red
 G = Green
 B = Blue
 C = Cyan
 M = Magenta
 Y = Yellow
 All values for R, G, B
and C, M, Y must first
be normalized.
24
CMY – CMYK Transformations
 The following matrices will perform transformations between
CMY and CMYK color spaces.
 Note that: C = Cyan
 M = Magenta
 Y = Yellow
 K = blacK
 All values for R, G, B
and C, M, Y, K must first
be normalized.
25
RGB – CMYK Transformations
 The following matrices perform transformations between RGB
and CMYK color spaces.
 Note that: R = Red
 G = Green
 B = Blue
 C = Cyan
 M = Magenta
 Y = Yellow
 All values for R, G, B
and C, M, Y must first
be normalized.
26
RGB – Gray Scale
Transformations
 The luminancy component, Y, of each color is summed to create
the gray scale value.
 ITU-R Rec. 601-1* Gray scale:
Y = 0.299R + 0.587G + 0.114B
 ITU-R Rec. 709 D65 Gray scale
Y = 0.2126R + 0.7152G + 0.0722B
 ITU standard D65 Gray scale (Very close to Rec 709!)
Y = 0.222R + 0.707G + 0.071B
*601-1: Based on an old television (NTSC: National Television System Committee) standard
709 : Based on High Definition TV colorimetry (Contemporary CRT phosphors)
ITU : International Telecommunication Union
27
RGB and CMYK Deficiencies
 RGB and CMY color models
0.8
limited to brightest available
primaries (R, G, and B) and
secondaries (CYM).
0.6
 Not intuitive. We think of light in
terms of color, intensity of color,
y
and brightness.
0.4
 Colors changed by changing
R, G, B ratios.
 Brightness changed by
0.2
changing R, G, and B, while
maintaining their ratios.
 Intensity changed by
0
projecting RGB vector toward
largest valued primary color
(R, G, or B).
Photographi
c film gamut
6 color
CMY printer
gamut
Monitor RGB
gamut
0
0.2
0.4
x
0.6
0.8
Hexachrome: Cyan, Magenta, Yellow, Black, Orange, Green
28
HSI / HSL / HSV*
 Very similar to the way human visions see color.
 Works well for natural illumination, where hue changes
with brightness.
 Used in machine color vision to identify the color of
different objects.
 Image processing applications like histogram operations,
intensity transformations, and convolutions operate on only
an image's intensity and are performed much easier on an
image in the HSI color space.
*H=Hue, S = Saturation, I (Intensity) = B (Brightness), L = Lightness, V = Value
29
HSI Color Space
 Hue
 What we describe as the color of
the object.
 Hues based on RGB color space.
 The hue of a color is defined by
its counterclockwise angle from
Red (0°); e.g. Green = 120 °, Blue
= 240 °.
 Saturation
 Degree to which hue differs from
neutral gray.
 100% = Fully saturated, high
contrast between other colors.
 0% = Shade of gray, low contrast.
 Measured radially from intensity
axis.
Blue
240º
Red 0º
Green
120º
RGB viewed
Color Space
RGB cube
from
gray-scale axis, and
HSI
Color
Wheel
RGB
cube
viewed
rotated
30° from
gray-scale axis
0%
Saturation
100%
30
HSI Color Space
 Intensity
Intensity
100%
 Brightness of each Hue, defined by
its height along the vertical axis.
 Max saturation at 50% Intensity.
 As Intensity increases or decreases
from 50%, Saturation decreases.
 Mimics the eye response in nature;
As things become brighter they look
more pastel until they become
washed out.
Hue
 Pure white at 100% Intensity. Hue
and Saturation undefined.
 Pure black at 0% Intensity. Hue
and Saturation undefined.
100%
0%
Saturation
0%
31
HSI Image Data
Full Image
Hue Channel
Saturation Channel
Intensity Channel
32
HSI - RGB
 For a given RGB color of (R, G, B), the same color in the HSI Model
is C(x,y) = (H, S, I), where
 Hue
 where
 Saturation
 Intensity
33
RGB to HSI Example
 Consider the RGB color defined by (215, 97,198)
R = 215, G = 97, B = 198
Blue
(0,0,255)
Green
(0,255,0)
Red
(255,0,0)
Red
0º
Blue
240º
Green
120º
Therefore, HSI coordinates = (308.64°, 0.843, 0.67)
34
HSI to RBG
 Dependent on which sector H lies in.
Blue
240º
Red
0º
For 240º  H  360 º
H  H  240
1
g  1  S 
3
1
S cos H 
b  1 
3  cos(60  H ) 
r  1  ( g  b)
Green
120º
For 0º  H  120 º
1
b  1  S 
3
1
S cos H 
r  1 
3  cos(60  H ) 
g  1  ( r  b)
For 120º  H  240 º
H  H  120
1
r  (1  S )
3
1
S cos( H ) 
g  1 
3  cos(60  H ) 
b  1  (r  g )
35
HSV Color Space
 Hue and Saturation similar to that
of HSI color model.
100%
Value
 V: Value; defined as the height
along the central vertical axis.
 Like Intensity in HSI, color intensity
increases as Value increases.
Hue
0%
100%
HSV: Hue, Saturation, and Value
Saturation
0%
36
HSV Color Space
 Hue and Saturation similar to that
of HSI color model.
 V: Value; defined as the height
along the central vertical axis.
Value
Intensity
Smax at V100
 Like Intensity in HSI, color intensity
increases as Value increases.
Smax at I50
 As Value increases, hues become
more saturated. Hues do not
progress through the pastels to
white, just as fluorescent images
never change colors even though
its intensity may increase. HSV is
good for working with fluorescent
colors.
HSV: Hue, Saturation, and Value
37
Intensity Operations in HSI
 To change the individual color
of any region in the RGB
image, change the value of
the corresponding region in
the Hue image.
 Then convert the new H
image with the original S and I
images to get the transformed
RGB image.
 Saturation and Intensity
components can likewise be
manipulated.
Original Image
Hue
Saturation
Intensity
38
Disadvantages of HSI Color
Model
There are many disadvantages to the HS color model. For example:
 Cannot perform addition of colors expressed in polar
coordinates. Transformations are very difficult because Hue is
expressed as an angle.
 For color machine vision, the hues of low-saturation may be
difficult to determine accurately. Systems which must be able
to differentiate all colors, saturated and unsaturated, will have
significant problems using the HSI representation.
 When saturation is zero, hue is undefined.
 Transforming between HSI and RGB is complicated.
39
1931 CIE* Standard Observer
(r, g, b)
 The following color
matching functions
were obtained.
0.4
0.3
r
 There were problems
with the r, g, b color
matching functions.
 Negative values meant
that the color had to be
added to the test light
before the two halves
could be balanced.
*Commission Internationale de L’Éclairage
Tristimulus values
b
g
0.2
0.1
0.0
-0.1
380
480
580
Wavelength (nm)
680
780
Color-matching functions for 1931 Standard Observer, the
average of 17 color-normal observers having matched
each wavelength of the equal energy spectrum with
primaries of 435.8nm, 546.1 nm, and 700 nm, on a bipartite
2° field, surrounded by darkness.
40
1931 CIE Standard Observer
(x, y, z)
 Special properties of X, Y, Z:-
2.0
z
Tristimulus values
 CIE adopted another set of
primary stimuli, designated as
X, Y, and Z.
 Imaginary (non-physical)
primary.
 All luminance information is
contributed by Y.
 Linearly related to R, G, B.
 Non-negative values for all
tristimulus values.
1.5
y
x
1.0
0.5
0.0
380
480
580
Wavelength (nm)
680
780
1931 standard observer (2° observer).
41
CIE 1931 xy Chromaticity
Diagram
 2D projection of 3D CIE XYZ
color space onto X+Y+Z=1
plane.
 x and y calculated as follows:-
 The chromaticity of a color is
determined by (x,y).
42
CIE 1931 xy Chromaticity
Diagram
 For color C, where
C  0.5 X + 0.4 Y + 0.1 Z
(0.5, 0.4)
 Color C is represented as
(0.5, 0.4) on the Chromaticity
diagram.
43
CIE 1931 xyY Chromaticity
Diagram
 Each point on xy corresponds to
many points in the original 3D
CIE XYZ space.
 Color is usually described by
xyY coordinates, where Y is the
luminance, or lightness
component of color.
 Y starts at 0 from the white spot
(D65) on the xy plane, and
extends perpendicularly to 100.
 As the Y increases, the colors
become lighter, and the range of
colors, or gamut, decreases.
44
CIE XYZD65 to sRGB*
 The following transformations allow transformations between
CIE XYZD65 and the sRGB color models.
*sRGB = Standard RGB, the standard for Internet use.
45
CIE XYZ
Rec. 609-1
- RGB
 The following are the transformations needed to convert between
CIE XYZRec.609-1 and RGB.
46
CIE XYZ - RGBRec. 709
 Use the following matrices to transform between CIE
XYZ and Rec. 709 RGB (with its D65 white).
 R709   3.240479 1.537150 0.498535  X 
G    0.969256 1.875992
 Y 
0.041556
 709  
 
 B709   0.055648 0.0204043 1.057311   Z 
 X   0.412453 0.357580 0.180423  R709 
 Y    0.212671 0.715160 0.072169 G 
  
  709 
 Z  0.019334 0.119193 0.950227   B709 
47
XYZ
D65
- XYZ
D50
Transformations
 If the illuminant is changed from D50 to D65, the observed
color will also change.
 The following matrices enable transformations between XYZD65
and XYZD50.
X 
 0.9555 0.0231 0.0633  X 
 Y    0.0283 1.0100 0.0211  Y 
 

 
 Z  D 65  0.0123 0.0206 1.3303   Z  D 50
48
Inadequacies in the 1931 xy
Chromaticity Diagram
 Each line in the diagram
represents a color difference of
equal proportion.
 The lines vary in length,
sometimes greatly, depending
on what part of the diagram
they're in.
 The differences in line length
indicates the amount of
distortion between parts of the
diagram.
49
CIE 1960 u,v Chromaticity
Diagram
 To correct for the deformities in the
1931 xy diagram, a number of
uniform chromaticity scale (UCS)
diagrams were proposed.
 The following formula transforms
the XYZ values or x,y coordinates
to a set of u,v values, which
present a visually more accurate
2D model.
50
CIE 1976 u', v' Chromaticity
Diagram
 But the 1960 uv diagram was
still unsatisfactory.
 In 1975, CIE modified the u,v
diagram and by supplying new
(u',v') values. This was done by
multiplying the v values by 1.5.
Thus in the new diagram u' = u
and v' = 1.5v.
 The following formulas allow
transformation between u’v’ and
xy coordinates.
51
CIE 1976 u', v' Chromaticity
Diagram
 Each line in the diagram
represents a color
difference of equal
proportion.
 While the representation
is not perfect (it can never
be), the u',v' diagram
offers a much better visual
uniformity than the xy
diagram.
52
CIE L*u*v* Color Space/
CIELUV
 Replaces uniform lightness
scale Y with L*, an visually
linear scale.
 Equations are as follows:-
where un’ and vn’ refer the the
reference white light or light
source.
53
CIE L*a*b* Color Space /
CIELAB
 Second of two systems adopted by CIE in
1976 as models that better showed
uniform color spacing in their values.
 Based on the earlier (1942) color
opposition system by Richard Hunter
called L, a, b.
 Very important for desktop color.
 Basic color model in Adobe PostScript
(level 2 and level 3)
CIE L*a*b* color axes
 Used for color management as the device
independent model of the ICC* device
profiles.
*International Color Consortium
54
CIE L*a*b* (cont’d)
 Central vertical axis : Lightness (L*),
runs from 0 (black) to 100 (white).
 a-a' axis: +a values indicate amounts of
red, -a values indicate amounts of green.
 b-b' axis, +b indicates amounts of yellow;
-b values indicates amounts of blue. For
both axes, zero is neutral gray.
 Only values for two color axes (a*, b*)
and the lightness or grayscale axis (L*)
are required to specify a color.
 CIELAB Color difference, E*ab, is
between two points is given by:
100
L*
-a
+b
-b
+a
(L1*, a1*, b1*)
(L2*, a2*, b2*)
0
CIE L*a*b* color axes
E *ab  (L*) 2  (a*) 2  (b*) 2
55
CIELAB Image Data
Full Color Image
L data
L-a channel
L-b channel
56
XYZ to CIELAB
 Given Xn, Yn, and Zn, which are the tristimulus values for the
reference white, for a point X, Y, Z:-
57
CIELAB to XYZ
 Reverse transformation to XYZ, given L*a*b* values.
For
58
CIE L*C*h* (LCh)
 Often referred to simply as LCh.
 Same system is the same as the
CIELab color space, except that it
describes the location of a color in
space by use of polar coordinates
rather than rectangular coordinates.
 L* is a measure of the lightness of a
sample, ranging from 0 (black) to 100
(white).
 C* is a measure of chroma (saturation),
and represents distance from the
neutral axis.
 h is a measure of hue and is
represented as an angle ranging from
0° to 360.
L* (Lightness)
100%
H (Hue)
0%
100%
C* (Chroma)
0%
59
Y’U’V’1 (EBU2) Color Space
 Red:
xR = 0.630
yR = 0.340
 Green:
xG = 0.310
yG = 0.595
 Blue:
xB = 0.155
yB = 0.070
 White
xW= 0.312713
yW = 0.329016
 Standard color space used for analogue television transmissions in
European TVs (PAL3 and SECAM4).
 Y is the luminance (or luma) or black and white component
 U and V represent the color differences: U = B - Y; V = R - Y
 U represents the Blue - Yellow axis; V, the Red - Green axis.
 Gamma for PAL is assumed to be 2.8
1
Y = Luminance, U and V are chrominance components
European Broadcasting Union
3 Phase Alternation Line video standard for Europe; U = 0.492(B-Y); V = 0.877(R-Y)
4 Sequential Couleur avec Mémoire, video standard for France, the Middle East and most of Eastern Europe
2
60
Y'UV Channels
Full Color Image
Y
U (Blue - Yellow)
V (Red - Green)
61
Nonlinear Y’U’V’
Transformations
 The following matrices allow transformations of nonlinear signals
between Y’U’V’ and R’G’B.
62
Linear Y’U’V’ Transformations
 The following matrices allow transformations of linear signals
between YUV RGB and XYZ.
63
Y’I’Q’1 Color Space
 Red:
xR = 0.67
yR = 0.33
 Green:
xG = 0.21
yG = 0.71
 Blue:
xB = 0.14
yB = 0.08
 White
xW= 0.310063
yW = 0.316158
 Used in NTSC2 color broadcasting in USA; compatible
with black and white television, which only uses Y.
 U and V defines colors clearly, but do not align with
desired human perceptual sensitivities.
 Y [0..1] is the luminance (or luma) component.
 I [-0.523 .. 0.523] represents the Orange-Blue axis.
 Q [-0.596 .. 0.596] represents the Purple-Green axis.
1Y’I’Q’
= Luminance, In-phase, and Quadrature phase.
Television Standards Committee video standard for North America
2National
64
YIQ Channels
Full Color Image
Y Channel
I (Orange - Blue)
Q (Purple - Green)
65
Y’I’Q’ – R’G’B’
 Use the following matrices to transform linear signals between
Y’I’Q’ and gamma-corrected RGB values.
66
YIQ - YUV
 YIQ - YUV transformation is simply a color rotation of 33º.
 The following matrices can be used to transform between
NTSC based YIQ and PAL based YUV.
YYUV  1.000 0.000 0.000  YYIQ 
 U   0.000 1.270 1.8050   I 

 


 V  0.000 0.9489 0.6561  Q 
0.000
0.000  YYUV 
YYIQ  1.000
 I   0.000 0.2676 0.7361  U 

 


 Q  0.000 0.3869 0.4596  V 
67
Y’CbCr* Color Space
Independent of scanning standard and
system primaries, therefore: No chromaticity coordinates.
 No CIE XYZ matrices.
 No assumptions about white point.
 No assumptions about CRT gamma.
 Y’ is luminance, Cb is the chromaticity component for blue, and Cr
is the chromaticity component for red.
 Very closely related to the YUV, it is a scaled and shifted YUV.
Cb = (B - Y) / 1.772 + 0.5
Cr = (R - Y) / 1.402 + 0.5
 Chrominance values Cb and Cr are [ 0..1 ].
 Deals only with digital representation of R’G’B’ signals in Y’CbCr
form.
 Color format for JPEG1 and MPEG2.
1JPEG
= Joint Photography Experts Group
= Motion Pictures Experts Group
2MPEG
68
Y'CbCr - RGB[0..+1]
 Use the following matrices to convert between YCbCr
and RGB ranging from [0 .. +1]
'
Y601
  16   65.481 128.553 24.996   R ' 
    
 G '
C

128


37.797

74.203
112.000
 B   
 
 CR  128 112.000 93.786 18.214  B ' 
 
'
  16  
0
0.00625893   Y601
 R '  0.00456621
G '  0.00456621 0.00153632 0.00318811   C   128 
  
 B   
 B '  0.00456621 0.00791071
   CR  128 
0


69
ITU-R.601 YCbCr - R’G’B’219
 ITU-R.601 defines 16 =< Y >= 235, and 16 =< Cb and Cr >= 240,
with 128 corresponding to 0.
 These BT.601 equations are used by many video ICs to convert
between digital R’G’B’ and BT.601 YCbCr data.
 R '  1.000 0.000 1.371   Y  16 
G '  1.000 0.336 0.698 C  128
  
 b

 B '  1.000 1.732
0.000  Cr  128
 Y   0.301 0.586 0.113   R '   0 
C   0.172 0.340 0.512  G '  128
 b 
   
Cr   0.512 0.430 0.082  B '  128
 The R’G’B’ values produced have a nominal range of 16 - 235.
ITU-R.601 = International Telecommunication Union – Radio communications Recommendation 601
RGB219 = A restricted color space used to match YUV standard transmission values
70
ITU-R.601 YCbCr - R’G’B’0-255
 If 24 bit R’G’B’ data needs to have a range of 0-255, the following
equation should be used.
 The R’, G’, and B’ values must be saturated at the 0 and 255
values.
 R '  1.164 0.000 1.596   Y  16 
G '  1.164 0.392 0.813 C  128
  
 b

 B '  1.164 2.017 0.000  Cr  128 
 Y   0.257 0.504 0.098   R '  16 
C   0.148 0.291 0.439  G '  128
 b 
   
Cr   0.439 0.368 0.071  B '  128
71
YCbCr 4:4:4
 Full resolution
 YCbCr 4:4:4 is in
uncompressed data format.
 Each pixel has all Y, Cb and
Cr values.
 Chrominance data can be
subsampled without
significant degradation in
image quality.
Y
Y
Y
Y
Cb C r Cb Cr Cb Cr Cb Cr
Y
Y
Y
Y
Cb C r Cb Cr Cb Cr Cb Cr
Y
Y
Y
Y
Cb C r Cb Cr Cb Cr Cb Cr
Y
Y
Y
Y
Cb C r Cb Cr Cb Cr Cb Cr
YCbCr 4:4:4
72
YCbCr 4:2:2
 Obtained by a 2:1 horizontal
subsampling of YCbCr 4:4:4
values.
 Often used digital cameras,
and many video ICs.
 Restore original colors by
interpolating missing Cb and
Cr values from the values
present.
Y
Y
Y
Y
Cb C r Cb Cr Cb Cr Cb Cr
Y
Y
Y
Y
Cb C r Cb Cr Cb Cr Cb Cr
Y
Y
Y
Y
Cb C r Cb Cr Cb Cr Cb Cr
Y
Y
Y
Y
Cb C r Cb Cr Cb Cr Cb Cr
YCbCr 4:2:2
4:4:4
73
YCbCr 4:2:0
 YCbCr 4:2:0 obtained by a
2:1 horizontal and vertical
subsampling of YCbCr 4:4:4
values.
 YCbCr (or, often called
“YUV”) values are often
subsampled to 4:2:0 before
JPEG compression.
 Restore original colors by
interpolating missing Cb and
Cr values from available
values.
Y
Y
Y
Y
Cb C r Cb Cr Cb Cr Cb Cr
Y
Y
Y
Y
Cb C r Cb Cr Cb Cr Cb Cr
Y
Y
Y
Y
Cb C r Cb Cr Cb Cr Cb Cr
Y
Y
Y
Y
Cb C r Cb Cr Cb Cr Cb Cr
YCbCr 4:2:0
4:4:4
74
YCbCr 4:1:1
 YCbCr 4:1:1 obtained by a
4:1 horizontal subsampling
of YCbCr 4:4:4 values.
 VHS* quality color.
Y
Y
Y
Y
Cb C r Cb Cr Cb Cr Cb Cr
Y
Y
Y
Y
Cb C r Cb Cr Cb Cr Cb Cr
Y
Y
Y
Y
Cb C r Cb Cr Cb Cr Cb Cr
Y
Y
Y
Y
Cb C r Cb Cr Cb Cr Cb Cr
YCbCr 4:1:1
4:4:4
VHS: Video Home System
75
YCbCr 4:2:2 - RGB
1. Convert YCbCr 4:2:2 to YCbCr 4:4:4, through
interpolation.
Y
Cb Cr
Y
Y
Cb Cr
Y
Y
Y
Y
Cb Cr Cb Cr Cb Cr Cb Cr
Y
Cb Cr
Y
Y
Y
Cb Cr
Y
Y
Cb Cr
Y
Y
Cb Cr
Y
Y
Y
Y
Y
Cb Cr Cb Cr Cb Cr Cb Cr
Y
Cb Cr
Y
Y
Cb Cr
Y
Y
Y
Y
Y
Cb Cr Cb Cr Cb Cr Cb Cr
YCbCr 4:2:2
Interpolation
of Cb and Cr
values
Y
Y
Y
Y
Cb Cr Cb Cr Cb Cr Cb Cr
YCbCr 4:4:4
76
YCbCr 4:2:2 - RGB
1. Convert YCbCr 4:2:2 to YCbCr 4:4:4, through
interpolation.
2. Convert YCbCr 4:4:4 to nonlinear R’G’B’.
77
YCbCr 4:2:2 - RGB
1. Convert YCbCr 4:2:2 to YCbCr 4:4:4, through
interpolation.
2. Convert YCbCr 4:4:4 to nonlinear R’G’B’.
3. If necessary, convert nonlinear R’G’B’ to
linear RGB by removing gamma information.
For (R’, G’, B’) < 21
 R'

255
  255
R
 4.5 


G'

255
  255
G 
 4.5 


 B'

255
  255
B
 4.5 


For (R’, G’, B’)  21
 R'
 0.099 
255

R  255  
1.099




2.2
 R'
 0.099 
255

G  255  
1.099




 R'
 0.099 
255

B  255  
1.099




2.2
2.2
78
SMPTE*-C RGB Color Space
 Red:
xR = 0.630
yR = 0.340
 Green:
xG = 0.310
yG = 0.595
 Blue:
xB = 0.155
yB = 0.070
 White
xW= 0.312713
yW = 0.329016
 Current color standard for broadcasting in America, replacing older
NTSC standard.
 Reason for standard change: original set of (YIQ) primaries being
slowly changed to YUV primaries.
 CRT gamma assumed to be 2.2 with NTSC, 2.8 with PAL.
*Society of Motion Picture and Television Engineers
79
Linear SMPTE-C RGB
Transformations
 The following matrices allow transformations of linear signals
between SMPTE-C RGB and XYZ.
80
Nonlinear SMPTE-C RGB
Transformation
 The transformation matrices for non-linear signals are the same as
that of the older YIQ (NTSC) standard.
81
ITU.BT-709 in Y'CbCr
 Red:
xR = 0.64
yR = 0.33
 Green:
xG = 0.30
yG = 0.60
 Blue:
xB = 0.15
yB = 0.06
 White (D65):
xW= 0.312713
yW = 0.329016
 Recent standard, defined only as an interim standard
for HDTV studio production.
 Defined by the CCIR (now the ITU-R) in 1988, but is
not yet recommended for use in broadcasting.
 The primaries are the R and B from the EBU, and a G
which is midway between SMPTE-C and EBU.
 CRT gamma is assumed to be 2.2.
ITU: International Telecommunication Union
CCIR: Comite Consultatif International des Radiocommunications
82
Linear XYZ Rec.709 – RGBD65
 The following matrices allow transformation between
linear signals of Rec.709 XYZ values and RGBD65.
 X 709  0.412 0.358 0.180   RD 65 
 Y    0.213 0.715 0.072  G 
 709  
  D 65 
 Z 709  0.019 0.119 0.950   BD 65 
 RD 65   3.241 1.537 0.499  X 709 
G    0.969 1.876
Y 
0.042
 D 65  
  709 
 BD 65   0.056 0.204 1.057   Z 709 
83
RGBEBU – RGB709
 The following matrices allow transformation between linear Rec.
709 RGB signals and EBU* RGB signals.
 R709  1.0440 0.0440 0.0000   REBU 
G    0.0000 1.0000 0.0000  G 
 709  
  EBU 
 B709   0.0000 0.0119 1.0119   BEBU 
European Broadcasting Union
84
Nonlinear Y’CbCr 709– R’G’B’
 The following matrices allow transformation between nonlinear Rec.709
Y’CbCr signals and R’G’B’.
 Scaling optimized for digital video.
 R '  1.0000 0.0000 1.5701   Y ' 
G '  1.0000 0.1870 0.4664 C 
  
 b
 B '   1.000 1.8556 0.0000  Cr 
0.0721   R ' 
 Y '   0.2215 0.7154
C    0.1145 0.3855 0.5000  G '
 b 
 
Cr   0.5016 0.4556 0.0459   B ' 
85
SMPTE-240M Y’PbPr (HDTV*)
 Red:
xR = 0.67
yR = 0.33
 Green:
xG = 0.21
yG = 0.71
 Blue:
xB = 0.15
yB = 0.06
 White
xW= 0.312713
yW = 0.329016
 This one of the developments of NTSC component coding, in which
the B primary and white point were changed. With this space color,
all three components Y’, Pb, and Pr are linked to luminance.
 Standard for coding High Definition TV broadcasts in the USA.
 The CRT gamma law is assumed to be 2.2.
*High Definition TeleVision
86
RGB
240M
- RGB
709
 The following transforms between SMPTE* 240M
(SMPTE RP 145 or Y'PbPr) RGB to Rec. 709 RGB.
R
 1.065364 0.055391 0.009974   R 
G 
 0.019635 1.036361 0.016725 G 

 

 
 B  240 M  0.001632 0.004414 0.993954   B  709
R
 0.939555 0.050173 0.010272   R 
G    0.017775 0.965795 0.016430  G 
 

 
 B  709  0.001622 0.004371 1.005993   B  240 M
*Society of Motion Picture and Television Engineers
240M = Recommended Standard for USA’s HDTV
87
RGB
240M
- RGB
EBU
 The following transforms from SMPTE 240M (SMPTE
RP 145, or YPbPr) RGB into to Rec. 709 RGB.
 REBU   1.3481 0.3481 0.0000   R240 
G    0.0257 1.0257 0.0000  G 
 EBU  
  240 
 BEBU   0.0254 0.0568 1.0822   B240 
 R240   0.7466 0.2534 0.0000  REBU 
G   0.0187 0.9813 0.0000 G 
 240  
  EBU 
 B240   0.0185 0.0575 0.9240  BEBU 
88
Linear SMPTE-240M XYZ - RGB
 The following matrices allow linear transformations
between SMPTE-240M XYZ and RGB.
 R   2.041 0.564 0.345  X 
G   0.893 1.816 0.032   Y 
  
 
 B   0.064 0.130 0.982   Z 
89
Nonlinear SMPTE-240M Y’PbPr
Transformations
 The following matrices allow nonlinear transformations between
Y’PbPr and R’G’B’.
 Scaling suited for component analogue video.
90
Xerox Corporation Y’E’S’1
 Standard proposed by Xerox Corporation.
 YES has three components:
 Y, or luminancy,
 E, or chrominancy of the red-green axis, and
 S, chrominancy of the yellow-blue axis.
 The following examples assume a CRT gamma of 2.2.
1YES
= Luminance, E = red-green chromaticity, S = blue-yellow chromaticity
91
Y’E’S’ to XYZD50 Transformation
 If you start with non-linear Y’E’S’ values, apply a gamma
correction to convert to linear YES values first:-
 Next, apply the following transformation to the linear YES.
92
XYZD50 to YES Transformation
 First, apply the following transformation matrix to obtain linear
YES from XYZD50.
 For non-linear Y’E’S’ values, apply a gamma correction.
93
YES to XYZD65 Transformation
 As before, if you start with non-linear Y’E’S’ values, apply a
gamma correction to convert to linear YES values first:-
 Next, apply the following transformation to the linear YES.
94
XYZD65 to YES Transformation
 First, apply the following transformation matrix to obtain linear
YES from XYZD50.
 If required, apply a gamma correction to obtain Y’E’S’.
95
Kodak Photo CD YCC (YC1C2)
Color Space
 Based on Rec. 709 and 601-1, the YCC color space has color
gamut defined by the Rec. 709 primaries and a luminance chrominance representation of color like ITU 601-1's YCbCr.
 YCC provides a color gamut that is greater than that which can
currently be displayed, and is therefore suitable not only for
both additive and subtractive (RGB and CMY(K)) reproduction.
 Extended color gamut obtainable by the PhotoCD system is
achieved by allowing both positive and negative values for
each primary, allowing YCC to store more colors than current
display devices, such as CRT monitors and dye-sublimation
printers, can produce.
96
Transformations to Encode
Kodak YC1C2 Data
 First, apply a gamma correction:
For R709, G709, B709  0.018
For R709, G709, B709  0.018
 Next, transform the R’G’B’ data into YC1C2 data.
 Scaling is optimized for films.
97
Transformations to Encode
YC1C2 Data (cont’d)
 Finally, store the floating point values as 8-bit integers.
 The unbalanced scale difference between Chroma1 and
Chroma2 is designed, according to Kodak, to follow the typical
distribution of colors in real scenes.
98
Transforming YC1C2 Data to
24-bit RGB
 Kodak YCC can store more information than current display
devices can cope with (it allows negative RGB values), so the
transforms from YCC to RGB are not simply the inverse of RGB
to YCC, they depend on the target display system.
First, recover normal Luma (Y) and Chroma (C1 and C2) data.
Second, if the display primaries match Rec. 709 primaries in
their chromaticity, then
99
YC1C2 – RGB Signal Voltages
 First, recover normal Luma (Y) and Chroma (C1 and C2).
 Then, calculate the RGB display voltages as follows;
VR ' 
1.000 0.000 1.000   Y 
V   1 1.000 0.194 0.509  C 
 G '  353.2 
 1
VB ' 
1.000 1.000 0.000  C2 
100
PhotoYCC - YCbCr
 Transform PhotoYCC color
space into YCbCr values as
follows:-
YYCbCr  1.402YYCC
Cb  1.291C1  73.400
Cr  1.340C2  55.638
 As the PhotoYCC color space is larger than the YCbCr color
space, the produced image may be poorer than the original.
 Transform YCbCr data into
PhotoYCC color space as
follows:-
YYCC  0.713YYCbCr
C1  0.775Cb  56.855
C2  0.746Cr  41.521
 The image produced may not match an image that was one
encoded directly in PhotoYCC color space.
101
sRGB specs
CIE chromaticities for ITU-R BT.709 reference primaries
and CIE standard illuminant
x
y
z
Red
0.6400
0.3300
0.0300
Green
0.3000
0.6000
0.1000
Blue
0.1500
0.0600
0.7900
D65 White Point
0.3127
0.3290
0.3583
sRGB Viewing Environment Summary
Condition
sRGB
Display Luminance level
80 cd/m2
Display White Point
x = 0.3127, y = 0.3290 (D65)
Display model offset (R, G and B)
0.0
Display input/output characteristic
2.2
Reference ambient illuminance level
64 lux
Reference Ambient White Point
x = 0.3457, y = 0.3585 (D50)
Reference Veiling Glare
0.2 cd m-2
102
Glossary of Color Models
Color
Space
Primaries
White pt
Gamma
Rx
Ry
Gx
Gy
Bx
By
Wx
Wy
Std name
Apple RGB
Trinitron
D65
1.8
0.63
0.34
0.28
0.6
0.16
0.07
0.31271
0.32902
Trinitron
D65
2.2
0.63
0.34
0.31
0.6
0.16
0.07
0.31271
0.32902
?
D65
2.2
0.64
0.33
0.3
0.6
0.15
0.06
0.31271
0.32902
CCIR 709
0.32902 CIE_XYZitu
SMPTE-C
sRGB
SMPTE-C
(CCIR 601-1)
HDTV
(CCIR 709)
Pal/Secam
EBU/ITU
D65
2.2
0.64
0.33
0.29
0.6
0.15
0.06
0.31271
Color Match
RGB
P22-EBU
D50
1.8
0.63
0.34
0.3
0.61
0.16
0.08
0.3457
0.3585
P22-EBU
Adobe RGB
Adobe RGB
(1998)
D65
2.2
0.64
0.33
0.21
0.71
0.15
0.06
0.31271
0.32902
Adobe RGB
(1998)
NTSC (1953)
NTSC (1953)
Std Illmnt C
2.2
0.67
0.33
0.21
0.71
0.14
0.08
0.31006
0.31616 CCIR 601-1
CIE RGB
CIE RGB
Std Illmnt E
2.2
0.74
0.27
0.27
0.72
0.17
0.01
0.3333
brightness
lightness
luma
chroma
saturation
0.3333
CIE RGB
- the human sensation by which an area exhibits more or less light.
- the sensation of an area's brightness relative to a reference white in the scene.
- Luminance component corrected by a gamma function and often noted Y'.
- the colorfulness of an area relative to the brightness of a reference white.
- the colorfulness of an area relative to its brightness.
CCIR: Comite Consultatif International des Radiocommunications
103
Glossary of Illuminants and
Their Reference Whites
Illuminant
A
B
C
D5500
D6500
D7500
E
wx
0.488
0.348
0.310
0.332
0.313
0.299
0.333
wy
0.407
0.352
0.316
0.348
0.329
0.315
0.333
104
2D Color Spaces
ITU Color Space
RGB Color Space
NTSC Color Space
HLS Color Space
SMPTE Color Space
HSV Color Space
Rec.709 Color Space
105
References
 BARCO Introduction to Color Theory, Monitor Calibration and
Color Management,
http://www.barco.com/display_systems/support/colorthe/colorthe.htm
 R. S. Berns, Principles of Color Technology (3rd Ed)., 2000
 S. M. Boker, The Representation of Color Metrics and Mappings in
Perceptual Color Space,
http://kiptron.psyc.virginia.edu/steve_boker/ColorVision2/ColorVision2.h
tml
 D. Bourgin, Color spaces FAQ,
http://www.inforamp.net/~poynton/notes/Timo/colorspace-faq, 1996,
 R. Buckley, Xerox Corp., G. Bretta, Hewlett-Packard Laboratories,
Color Imaging on the Internet,
http://www.inventoland.net/imaging/cii/nip01.pdf, 2001
 Color Representation,
http://203.162.7.85/unescocourse/computervision/comp_frm.htm
106
References (cont’d)
 A. Ford and A. Roberts, Color Space Conversions,
www.inforamp.net/~poynton/PDFs/coloureq.pdf, 1998
 Gonzales, Woods, Digital Image Processing, 2000
 A. Kankaanpaa, Color Formats,
www.physics.utu.fi/ett/kurssi/sfys3066/arto_tiivis.pdf, 2000.
 M. Nielsen and M. Stokes, Hewlett-Packard Company, The Creation
of the sRGB ICC Profile, http://www.srgb.com/c55.pdf
 C. Poynton, Frequently Asked Questions about Color,
http://www.inforamp.net/~poynton/ColorFAQ.html, 1999
 C. Poynton, Frequently Asked Questions about Gamma,
http://www.inforamp.net/~poynton/GammaFAQ.html, 1999
 G. Starkweather, Colorspace interchange using sRGB,
http://www.microsoft.com/hwdev/tech/color/sRGB.asp, 2001
107
The End
- Question and Answer Session -
108