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Lecture 8. Color Image Processing
EL512 Image Processing
Dr. Zhu Liu
zliu@research.att.com
Note: Part of the materials in the slides are from Gonzalezs DigitalImage Processingand Onurs lecture slides
Fall 2004 EL512 Image Processing Lecture 8, Page 2
Lecture Outline
Color perception and representation
Human perception of color
Trichromatic color mixing theory
Different color representations
Color image display
True color image
Indexed color images
Pseudo color images
Color image enhancement
Fall 2004 EL512 Image Processing Lecture 8, Page 3
Light is part of the EM wave
Fall 2004 EL512 Image Processing Lecture 8, Page 4
Human Perception of Color
Retina contains receptors
Cones
Day vision, can perceive color tone
Red, green, and blue cones
Rods
Night vision, perceive brightness only
Color sensation
Luminance (brightness)
Chrominance
Hue (color tone)
Saturation (color purity)
Fromhttp://www.macula.org/anatomy
/retinaframe.html
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Fall 2004 EL512 Image Processing Lecture 8, Page 5
Frequency Responses of Cones
ybgridaCC ii ,,,,)()( == Fall 2004 EL512 Image Processing Lecture 8, Page 6
Illuminating and Reflecting Light
Illuminating sources: emit light (e.g. the sun, light bulb, TV monitors)
perceived color depends on the emittedfrequency
Reflecting sources: reflect an incoming light (e.g. the color dye,
matte surface, cloth)
perceived color depends on reflected
frequency (=emitted frequency - absorbedfrequency
Fall 2004 EL512 Image Processing Lecture 8, Page 7
Tri-chromatic Color Mixing
Tri-chromatic color mixing theory Any color can be obtained by mixing three primary colors with a
right proportion
Primary colors for illuminating sources:
Red, Green, Blue (RGB) Color monitor works by exciting red, green, blue phosphors
using separate electronic guns
follows additive rule: R+G+B=White
Primary colors for reflecting sources (also known assecondary colors): Cyan, Magenta, Yellow (CMY)
Color printer works by using cyan, magenta, yellow and black(CMYK) dyes
follows subtractive rule: R+G+B=Black
Fall 2004 EL512 Image Processing Lecture 8, Page 8
RGB vs CMY
Magenta = Red + BlueCyan = Blue + GreenYellow = Green + Red
Magenta = White - GreenCyan = White - RedYellow = White - Blue
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Fall 2004 EL512 Image Processing Lecture 8, Page 9
Red
Green Blue
A Color Image
Fall 2004 EL512 Image Processing Lecture 8, Page 10
Tristimuls Values
Tristimulus valueThe amounts of red, green, and blue needed
to form any particular color are called thetristimulus values, denoted by X, Y, and Z.
Trichromatic coefficients
Only two chromaticity coefficients are
necessary to specify the chrominance of alight.
.,,ZYX
Zz
ZYX
Yy
ZYX
Xx
++=
++=
++=
1=++ zyx
Fall 2004 EL512 Image Processing Lecture 8, Page 11
CIE Chromaticity Diagram
CIE (CommissionInternationale deLEclairage,InternationalCommission onIllumination ) systemof color specification
x axis: redy axis: green
The point marked with GREEN
x: 25%, y: 62%, z: 13%.
Fall 2004 EL512 Image Processing Lecture 8, Page 12
Color Models
Specify three primary or secondary colorsRed, Green, Blue.
Cyan, Magenta, Yellow.
Specify the luminance and chrominanceHSB or HSI (Hue, saturation, and brightness or
intensity)
YIQ (used in NTSC color TV)
YCbCr (used in digital color TV)
Amplitude specification:8 bits per color component, or 24 bits per pixel
Total of 16 million colorsA 1kx1k true RGB color requires 3 MB memory
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Fall 2004 EL512 Image Processing Lecture 8, Page 13
RGB Color Model
RGB 24-bit color cube
Fall 2004 EL512 Image Processing Lecture 8, Page 14
CMY and CMYK Color Models
Conversion between RGB and CMY
Equal amounts of Cyan, Magenta, andYellow produce black. In practice, thisproduce muddy-looking black. To producetrue black, a fourth color, black is added,which is CMYK color model.
.
1
11
,
1
11
=
=
Y
MC
B
GR
B
GR
Y
MC
Fall 2004 EL512 Image Processing Lecture 8, Page 15
HSI Color Model
Hue represents dominant color asperceived by an observer. It is an attributeassociated with the dominant wavelength.
Saturation refers to the relative purity orthe amount of white light mixed with a hue.The pure spectrum colors are fullysaturated. Pink and lavender are lesssaturated.
Intensity reflects the brightness.
Fall 2004 EL512 Image Processing Lecture 8, Page 16
The HSI Color Model
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Fall 2004 EL512 Image Processing Lecture 8, Page 17
Conversion Between RGB and HSI
Converting color from RGB to HSI
Converting color from HSI to RGB
[ ]
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BGRI
BGRBGR
S
BGBRGR
BRGR
withGBif
GBifH
++=
++=
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RG sector (0H
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Fall 2004 EL512 Image Processing Lecture 8, Page 21
Demo Using Photoshop
Fall 2004 EL512 Image Processing Lecture 8, Page 22
Color Image Display and Printing
Display:
Need three light sources projecting red, green, blue
components respectively at every pixelAnalog display: raster scan
Digital display: directly projecting at all pixel locations
Printing:
Need three (or more) color dyes (Cyan, Magenta,Yellow, and Black)
Analog printing
Digital printing
Out of gamut color
Fall 2004 EL512 Image Processing Lecture 8, Page 23
Color Image Display
RedLUT
RedGun
GreenLUT
RedGun
Blue
LUT
Red
Gun
Red signal
Green signal
Blue signal
0
1
255
Input Output
Fall 2004 EL512 Image Processing Lecture 8, Page 24
Color Gamut
Each color model has different color range (or gamut). RGB model has a larger gamut than CMY.
Therefore, some color that appears on a screen may not be printable and is replaced by the closestcolor in the CMY gamut.
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Fall 2004 EL512 Image Processing Lecture 8, Page 25
Demo with Photoshop
Using photoshop to show how out of gamut color
(view->Gamut warning).
Fall 2004 EL512 Image Processing Lecture 8, Page 26
Gamma Correction
The intensity to voltage response curve ofthe computer monitor is not linear.
Gamma correction
Sample Input to Monitor
Output from Monitor
Graph of Input
Graph of Output L=V2.5
Sample Input to Monitor
Monitor Output
Gamma corrected Input
Graph of Input
Graph of Output
Graph of Correction L=L1/2.5
Fall 2004 EL512 Image Processing Lecture 8, Page 27
Color Quantization
Select a set of colors that are most frequently used in animage, save them in a look-up table (also known as colormap or color palette)
Any color is quantized to one of the indexed colors Only needs to save the index as the image pixel value
and in the display buffer
Typically: k=8, m=8 (selecting 256 out of 16 million)
Input index (k bits) Red color (m bits) Green color (m bits) Blue color (m bits)
Index 1
.
Index 2^k
Fall 2004 EL512 Image Processing Lecture 8, Page 28
Uniform vs. Adaptive Quantization
Uniform (scalar quantization)
Quantize each color component uniformly
E.g. 24 bit-> 8 bit can be realized by using 3 bits (8 levels) forred, 3 bits (8 levels) for green, 2 bits (4 levels) for blue
Do not produce good result
Adaptive (vector quantization)
Treating each color (a 3-D vector) as one entity. Findsthe Ncolors (vectors) that appear most often in agiven image, save them in the color palette
(codebook). Replace the color at each pixel by the
closest color in the codebook
The codebook (I.e. color palette) varies from image toimage -> adaptive
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Fall 2004 EL512 Image Processing Lecture 8, Page 29
Illustration of the Vector Quantization
x
y
x
y
Uniform Quantization Vector Quantization
Codebook size: 25
Fall 2004 EL512 Image Processing Lecture 8, Page 30
24 bits -> 8 bits
Adaptive (non-uniform) quantization(vector quantization)
Uniform quantization(3 bits for R,G, 2 bits for B)
Example of Color Image Quantization
Fall 2004 EL512 Image Processing Lecture 8, Page 31
Web Colors: 216 Safe RGB Colors
These colors are those that can be rendered consistently by different computer systems. They are obtainedby quantizing the R,G,B component independently using uniform quanitization. Each component is
quantized to 6 possible values: 0(0x00), 51(0x33), 102(0x66), 153(0x99), 204(0xCC), 255(0xFF).
Fall 2004 EL512 Image Processing Lecture 8, Page 32
Color Dithering
Color quantization may cause contour effect when thenumber of colors is not sufficient
Dithering: randomly perturb the color values slightly to
break up the contour effect fixed pattern dithering diffusion dithering (the perturbed value of the next pixel depends
on the previous one)
Developed originally for rendering gray scale image using blackand white ink only
Original
value(R,G, or B)
Ditheringvalue
Dithered
value
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Fall 2004 EL512 Image Processing Lecture 8, Page 33
8 bit uniformwithout dithering
8 bit uniformwith diffusion dithering
Example of Color Dithering
Fall 2004 EL512 Image Processing Lecture 8, Page 34
Demo Using Photoshop
Show quantization results with different methods
using image->mode->index color
PaletteSystem(Windows)
256 colors
Fall 2004 EL512 Image Processing Lecture 8, Page 35
Pseudo Color Image
Why?
Human eye is more sensitive to changes in
the color hue than in brightness.
How?
Use different colors (different in hue) to
represent different image features in amonochrome image.
Fall 2004 EL512 Image Processing Lecture 8, Page 36
Pseudo Color Display
Intensity slicing: Display different gray levelsas different colors
Can be useful to visualize medical / scientific /
vegetation imagery E.g. if one is interested in features with a certain intensity
range or several intensity ranges
Frequency slicing: Decomposing an image intodifferent frequency components and represent
them using different colors.
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Fall 2004 EL512 Image Processing Lecture 8, Page 37
Intensity Slicing
Pixels with gray-scale (intensity) value in the range of (fi-1 , fi) are rendered with color Ci
f0 =0
C3
C2
C1
f2f1 f3 f4
C4
Gray level
Color
Fall 2004 EL512 Image Processing Lecture 8, Page 38
Example
Fall 2004 EL512 Image Processing Lecture 8, Page 39
Another Example
Fall 2004 EL512 Image Processing Lecture 8, Page 40
Pseudo Color Display of Multiple Images
Display multi-sensor images as a single colorimage
Multi-sensor images: e.g. multi-spectral images by
satellite
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Fall 2004 EL512 Image Processing Lecture 8, Page 41
An Example
Fall 2004 EL512 Image Processing Lecture 8, Page 42
Example
Fall 2004 EL512 Image Processing Lecture 8, Page 43
Color Image Enhancement
Enhance each primary color componentindependently using the techniques formonochrome images
Will change the color hue of the originalimage
Convert the tri-stimulus representation intoa luminance / chrominance representation,and enhance the contrast of the luminancecomponent only.
Use HSI representation, where I truly reflectsthe luminance information.
Fall 2004 EL512 Image Processing Lecture 8, Page 44
Example of Color Image Enhancement
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Fall 2004 EL512 Image Processing Lecture 8, Page 45
Example of Color Image Enhancement
Fall 2004 EL512 Image Processing Lecture 8, Page 46
Example of Color Image Enhancement
Fall 2004 EL512 Image Processing Lecture 8, Page 47
Homework
1. (Computer Assignment) Write a program which first performs highpass filtering(you can use matlab func conv2 for this part) of an input gray scale image using thefollowing filter:
Scale the filtered image to range between 0 and 255. Then displays the filteredimage using 3 pseudo colors, using the following color transformation: Color Red forvalues 0-80, Color Green for values 81-160, color blue for values 161-255. In matlab,you can use the function colormapto change the colormap and use imshowtodisplay an image using a specified colormap. Comment on the visual effect, e.g.each color represents what attributes of the image?
2. (Computer Assignment) Choose a 24 bit RGB color image, perform the followingoperations: 1) convert it to YIQ format, save the resulting images in three separatefiles (for Y, I and Q components respectively), each with 8 bits/pixel; 2) performhistogram equalization to the Y image; 3) convert the enhanced Y image and theoriginal I and Q image back to the RGB image. View the original and enhanced colorRGB images and comment on your observations.
3. (Computer Assignment) Choose a 24 bit color RGB image, quantize the R, G, and Bcomponents to 3, 3, and 2 bits, respectively, using a uniform quantizer in the range0-256. Display the original and quantized color image using the original colormapassociated with the image. Comment on the difference in color accuracy. Make sureyou use a computer that has a 24 bit color display, and the test image has goodcolor contrast.
010
141
010
4
1
Fall 2004 EL512 Image Processing Lecture 8, Page 48
Reading
Prof. Yao Wangs Lecture Notes, Chapter6.
R. Gonzalez, Digital Image Processing,Chapter 6.
A. K. Jain, Fundamentals of Digital ImageProcessing, Section 3.7 ~ 3.11, 7.7 ~ 7.8.
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