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Color Gamut Matching for Tiled Display Walls

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Full Gamut Color Matching for Tiled
Display Walls
Grant Wallace, Han Chen, Kai Li
Princeton University
Talk Outline
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Introduction
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Previous Work
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Algorithm Description
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Algorithm Evaluation
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Questions and Comments
The Goal: Seamless Display Walls
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Geometric Alignment
Luminance Balancing
Color Correction
Causes of Color Imbalance
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Differing color primaries
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Differing RGB color proportions
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Projector bulbs
Color filters
Color temperature setting
RGB luminance mismatch
Contributing to these
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Manufacturing tolerances
Temporal decay
Differences in model/brand
Talk Outline
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Introduction
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Previous Work
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Algorithm Description
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Algorithm Evaluation
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Questions and Comments
Previous Work
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Majumder et al
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Generalized description of color balancing problem
Independent RGB channel balancing
Stone et al
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Algorithm to find common gamut of LCD projectors
Characterization of difficulties with DLP projectors
Independent channel balancing
New Challenges
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DLP projectors have non-additive color response.
Projectors of different model/brand may have different
primary chromaticity values.
Color Gamuts
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A color gamut is the set of reproducible colors.
(X, Y, Z) пЂЅ F(r, g, b)
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r, g, b пЂЅ 0...255
Color gamuts are device dependent
A collection of projectors are color balanced when
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A standard gamut is defined within the intersecton of the
individual gamuts
A standard color transfer function is used to map rgb triples
into the standard gamut
Additive Gamuts
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A gamut is additive if its color transfer function is
distributive under addition.
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Let F(r,g,b) be a device’s color response to a rgb input
If F(r,g,b) = F(r,0,0) + F(0,g,0) + F(0,0,b)
then F is defines an additive gamut.
Nice properties of additive gamuts
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The color transfer function F can be represented by a 3x3 matrix
Independent RGB channel balancing is effective at color balancing
Additive Gamut
Channel balancing is effective
on additive gamuts
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0
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DLP Projectors
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DLP projectors commonly use “white enhancement” to
increase the contrast ratio
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Four color filters are used – red, green, blue and clear (white)
White is added based on a function of the RGB input values
Similar to CMYK color printing
White enhancement creates a non-additive gamut
Typical Gamut of a DLP Projector
Channel balancing cannot completely match
non-additive gamuts
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0
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Mixed Vendor Projector Arrays
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Projectors from different vendors typically have different
primary chromaticity values
G
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y
0.6
0.5
W
0.4
R
0.3
0.2
0.1
0
0.1
B
x
Comparing CIE x-y Plot for a LCD and DLP Projector
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Channel balancing not effective for differing
primary chromaticity values
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-200
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Talk Outline
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Introduction
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Previous Work
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Algorithm Description
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Algorithm Evaluation
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Questions and Comments
Full Gamut Color Matching
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Generalized approach
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Treat projectors as black box
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Handle all cases
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Color transfer function and parameters unknown
Type and characteristics of projector unknown
Non-additive gamuts
Differing primary chromaticity values
Method
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Sub-sample the color response of each projector
Define a common gamut in the intersection of the
projector gamuts
Remap color transfer function into common gamut
Measuring the Color Transfer Function
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24 bit color = 16 Million colors (too many to
measure)
Subsample at a lower spatial frequency
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Non-uniform sampling grid
Use 32 increment for RGB < 128
Use 16 increment for RGB > 128
This gives 13 grid points per channel
0, 32, 64, 96, 128, 144, 160, 176, 192, 208, 224, 240,
255
133 (2197) sample points total
Finding a Standard Color Gamut
Choose an initial standard color gamut Gs
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Maximize the volume of Gs
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300
Constraint: Gs must be contained in the intersection of the
projector gamuts
i.e G s пѓЌ G 1 пѓ‡ G 2 пѓ‡ пЃЊ пѓ‡ G n
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We can choose to have a shape similar to the average gamut
Or we can choose any reference gamut (useful for mixed arrays)
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Projector Gamuts
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0
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180
200
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y
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140
0
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Standard gamut Gs highlighted
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Color Maps
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A color map Mi is needed for each projector to
emulate the standard gamut Gs
This map can be generated once Gs is defined
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Let Fi be a projector’s native color transfer function
Let Fs be the color transfer function that generates Gs
We want F i пЃЇ M i пЂЅ F s
Therefore M i пЂЅ F i пЃЇ Fs where M i : RGB п‚® RGB
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Mi must be pre-applied to all imagery
Applying Color Map
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Applying in CPU too costly
Latest graphics cards and libraries
can support color mapping
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Nvidia GeForce4 and ATI Radeon 9700
DirectX Pixel Shader and OpenGL
Texture Shader
Process
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Load color map M as a volume texture
RGB pixel value used as texture
coordinate into M, returns the mapped
color
Sample Cope
// Sample code ps 1.2
// t0 is the rendered texture
tex t0
// t1 is the color map
texreg2rgb t1, t0
// t2 is the luminance map
tex t2
mov r1, t1
mul r0, t2, r1
Talk Outline
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Introduction
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Previous Work
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Algorithm Description
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Algorithm Evaluation
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Questions and Comments
Experimental Setup
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Two test cases
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Measurement - Sequel Chroma IV colorimeter
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Inexpensive CIE XYZ measuring device ~ 200 USD
Average measurement consistency of 0.4% on DLP projectors
Channel balancing algorithm implemented for
comparison
Color corrected projectors remeasured
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Case 1: 4 DLP projectors
Case 2: 1 DLP and 1 LCD projector
Using same colorimeter
Lower spatial frequency 93 (729) samples
Error Metric
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Average deviation of a test color from the mean
Gives an indication of color consistency among projectors
Results Case 1: DLP Projector Array
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Display Wall of 4 Compaq MP1800 DLP projectors
Compare color consistency from three sets of measurements: No
correction, Channel balancing, and Full gamut matching
Color
None
ICB
FGCM
Red (R)
10.22%
1.78%
0.75%
Green (G)
7.36%
1.71%
0.77%
Blue (B)
10.75%
2.38%
1.20%
Cyan (C)
9.54%
2.41%
0.86%
Magenta (M)
11.02%
5.95%
1.61%
Yellow (Y)
8.64%
3.26%
1.14%
Black (K)
15.59%
19.48%
3.26%
White (W)
10.40%
11.12%
8.95%
3.40%
1.11%
Total
1.47%
Results Case 1: DLP Projector Array
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140
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y120
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Channel Balance Uncorrected
Correction Projector Gamuts
Full Gamut Correction
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x
x
0
120
140
y
Results Case 2: Mixed DLP-LCD Array
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Display Wall contains a Compaq MP1800 DLP projector and a Toshiba
TLP511U LCD projector
Compare color consistency from three sets of measurements: No
correction, Channel balancing, and Full gamut matching
Color
None
ICB
FGCM
Red (R)
17.03%
5.28%
1.33%
Green (G)
2.37%
3.90%
0.64%
Blue (B)
15.42%
8.25%
1.28%
Cyan (C)
11.20%
5.49%
0.82%
Magenta (M)
19.38%
7.46%
0.67%
Yellow (Y)
10.09%
2.66%
1.19%
Black (K)
50.22%
40.02%
11.93%
White (W)
32.54%
0.74%
Total
12.95%
8.28%
6.21%
1.27%
Results Case 2: Mixed DLP-LCD Array
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150
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z
z
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Full Gamut Correction
Channel Balance Correction
Uncorrected Projector Gamuts
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x
80
90
Results Case 2: Mixed DLP-LCD Array
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Uncalibrated CIE x-y Plot
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Channel Balanced CIE x-y Plot
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Full Gamut Correction CIE x-y Plot
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Performance Results
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We tested the performance of Full Gamut Color Mapping on two
platforms
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550 MHz Pentium III w/ GeForce4 card
3.06 GHz Pentium 4 w/ ATI Radeon card
We compare with the following transformations
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A – applies only geometric alignment
B – applies geometric alignment and alpha mask
C – applies geometric alignment with color map
D – applies geometric alignment, alpha mask and color map
Platform
A
B
C
D
550 MHz
P3/GeForce4
22.9
22.6
22.3
22.1
3.06 GHz
P4/Radeon
86.4
86.5
86.4
86.4
Performance of image viewer in Frames Per Second
Talk Outline
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Introduction
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Previous Work
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Algorithm Description
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Algorithm Evaluation
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Questions and Comments
The End
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Questions and Comments?
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Further Information
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www.cs.princeton.edu/omnimedia
Perceptible Color Differences
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DLP-LCD
Chromaticity Variance
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0.7
MacAdam ellipses
magnified 10 times
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