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Color Quantization

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got colors?
Color Quantization Techniques
Joo Hyun Song
1
Outline
•
•
•
•
Overview
Implementation
Results and Discussion
Concluding Remarks
2
Overview
• What is color quantization?
– “Color quantization is applied when the color
information of an image is to be reduced.“ 1
– “The most common case is when a 24-bit
color image is transformed into an 8-bit color
image.” 1
– Used when high-depth color is not supported
or necessary.
1
http://www.dai.ed.ac.uk/HIPR2/quantize.htm
3
Overview (cont’d)
• Main issues concerning color quantization
– What are the criteria for colors that are
retained in the resulting image?
(How are the �important’ colors selected?)
– How �accurate’ is the resulting image?
(How well are the important features of the
image preserved in the resulting image?)
– How fast is the quantization process?
4
Implementation
• Diversity Algorithm 2
– Color quantization algorithm devised by John Bradley,
the creator of the popular UNIX-based imaging
software xv.
– The algorithm starts by picking the most populous
color (the overall color) of the original image.
– Then the most �distant’ colors from the colors in the
new color table are picked until the new color table is
filled.
– Results in the most �diverse’ selection of colors
surrounding the overall color.
2 http://www.trilon.com/xv/manual/xv-3.10a/diversity-algorithm.html
5
Implementation (cont’d)
• Modified Diversity Algorithm 2
– Improvement over the original Diversity Algorithm
suggested by Tom Lane of the Independent JPEG
Group.
– The modification aims to better balance the allocation
between diverse colors and populous colors.
– The alternation strategy is subjective – some strategy
works better in certain images while not as good in
other.
– Examples in this presentation use the 10-div-pop/div
rule. пЃЉ
6
Implementation (cont’d)
• ImageMagickTM 3 libraries and tools
– Provides readily available libraries and tools for
reading, manipulating and writing most of the popular
image formats.
• Programming Language of choice: C++
– C++ has std::vector and std::map datatypes that
made the histogram and colorMap implementation
simpler.
– C++ has a built-in optimized sort() function that can
be used for sorting elements in std::vector datatypes.
– Not Java. ;-)
3
http://www.imagemagick.org/
7
Test Images
8
Results
• Tested Algorithms
– Diversity Algorithm
– Modified Diversity Algorithm
• Tested Color Spaces
–
–
–
–
–
RGB
YUV
YIQ
XYZ
U*V*W*
(not really)
9
Original Diversity vs. Modified Diversity
Original Diversity Algorithm (RGB)
Modified Diversity Algorithm (RGB)
10
Original Diversity vs. Modified Diversity
(cont’d)
Original Diversity Algorithm (RGB)
Modified Diversity Algorithm (RGB)
11
Original Diversity vs. Modified Diversity
(cont’d)
Original Diversity Algorithm (RGB)
Modified Diversity Algorithm (RGB)
12
Color Space Comparisons
Original
YUV
RGB
XYZ
13
Color Space Comparisons
Original
YUV
RGB
XYZ
14
Color Space Comparisons
Original
YUV
RGB
XYZ
15
Weird…
U*V*W* (supposedly)
16
My U*V*W* Implementation
double DecodeUVW(const unsigned int color, const char opt)
{
double X = DecodeXYZ(color, 'x');
double Y = DecodeXYZ(color, 'y');
double Z = DecodeXYZ(color, 'z');
double x = X/(X+Y+Z);
double y = Y/(X+Y+Z);
double u = 4*x/(-2*x+12*y+3);
double v = 6*y/(-2*x+12*y+3);
double W = 25*pow(100*Y, 1/3)-17;
// Get reference white
X = DecodeXYZ(0xFFFFFF, 'x');
Y = DecodeXYZ(0xFFFFFF, 'y');
Z = DecodeXYZ(0xFFFFFF, 'z');
x = X/(X+Y+Z);
y = Y/(X+Y+Z);
double u0 = 4*x/(-2*x+12*y+3);
double v0 = 6*y/(-2*x+12*y+3);
if(opt == 'u') {
return 13*W*(u-u0);
}
if(opt == 'v') {
return 13*W*(v-v0);
}
if(opt == 'w') {
return W;
}
}
17
Future Interest
• Investigate more advanced color spaces
(such as L*u*v* or IHS).
• Investigate other color metrics (e.g.
Riemannian color space).
• Investigate more advanced color
quantization algorithms (such as the
Neural Networks color quantization
algorithm).
18
Performance of other programs
• ImageMagick’s built-in quantizeColors() algorithm
19
Performance of other programs
(cont’d)
• Gimp
20
What I Have Learned
1. Start EARLY пЃЉ
2. Subjectivity of image quality.
3. Different Color Spaces.
4. Don’t use ImageMagick
21
Any Questions?
22
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