Color2Gray

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Color2Gray

• Imanol Gómez Rubio
• Computational Photography 11/Dec/2007
• TU-Berlin

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Index

• 1. Introduction
• 2. Converting to Grayscale
• 3. Color2Gray
• 3.1 Challenges
• 3.2 Algorithm
• 4. Future Work
• 5. Examples
• 6. references

Imanol Gómez Rubio Computational Photography
TU Berlin
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1. Introduction
Impressionist Sunrise by Claude Monet
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1. Int Isoluminant Colors
Color
Grayscale
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TU Berlin
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1. Int Featureless Conversion
Color
Grayscale
Color2Gray
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TU Berlin
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2. Converting to Grayscale

• In Color Space
• Linear
• Nonlinear
• In Image Space
• Pixels (RGB)
• Using colors in the image
• Different gray for different color
• Relative difference
• Using colors in the image and their position in
  image space
• Colors can map to same gray..

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TU Berlin
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2. Conv In ColorSpace
Color
Non linear mapping
Simple linear mapping
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TU Berlin
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2. Conv Related Work

• Previous Methods from Color2gray
• Based on changing to CIE Lab

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TU Berlin
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2. Conv Iluminance channels
Problem can not be solved by simply switching
to a different space
CIE CAM 97
Photoshop LAB
CIE XYZ
YCrCb
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TU Berlin
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3. Color2Gray

• This Method attempts to preserve the salient
  features of the color image and the relative
  differences.
• Human Perception

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TU Berlin
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3.1 Challenges 1

• Influence of Neighboring pixels

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TU Berlin
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3.1 Challenges 2

• Dimension and size reduction

120, 120
100
0
-120, -120
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TU Berlin
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3.1 Challenges 3

• Many Color2Gray Solutions

Original
. . .
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TU Berlin
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3.2 Algorithm Overview

• Convert to Perceptually Uniform Space
• CIE Lab
• Initialize image, g, with L channel
• For every pixel
• Compute Luminance distance
• Compute Chrominance distance
• Adjust g to incorporate both luminance and
  chrominance differences

?ij
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TU Berlin
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3.2 Algorithm Parameters
Radius of neighboring pixels
????
????
Max chrominance offset
????
Map chromatic difference to increases or
decreases in luminance values
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TU Berlin
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3.2 Algorithm ?
Color
? 9
Full Neighborhood
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TU Berlin
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3.2 Algorithm ?
?? 5
?? 15
?? 25
?? 35
?? 45
?? 55
?? 65
?? 75
?? 85
?? 95
Imanol Gómez Rubio Computational Photography
TU Berlin
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3.2 Algorithm ?
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TU Berlin
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3.2 Algorithm combining Chrominance and Luminance
(Luminance)
?Lij
if ?Lij gt ?Cij
?ij??
?Cij
(Chrominance)?
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TU Berlin
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3.2 Algorithm combining Chrominance and Luminance
?Lij
if ?Lij gt crunch(?Cij)?
??????ij??
crunch(?Cij)?
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TU Berlin
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3.2 Algorithm combining Chrominance and Luminance
?Lij
if ?Lij gt crunch(?Cij)?
??????ij??
if ?Cij . ?? 0
crunch(?Cij)?
otherwise
crunch(-?Cij)?
v? (cos ?, sin ?)?
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TU Berlin
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3.2 Algorithm Optimization

• min ? ? ( (gi - gj) - ?i,j ??

i?
ji-?
i
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TU Berlin
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4. Future Work

• Faster
• Multiscale

• Smarter
• Remove need to specify ?
• New optimization function designed to match both
  signed and unsigned difference terms
• Image complexity measures

• Animations/Video

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TU Berlin
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5. Examples
Color
Photoshop
Color2gray
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5. Examples
Color
Color2gray
Photoshop
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TU Berlin
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5. Examples
Color
Color2gray
Photoshop
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6. References

• http//www.color2gray.info
• Volk, C., 2000. Adobe Photoshop Tip of the Week
  Tutorial.
• http//www.carlvolk.com/photoshop21.htm.
• Rasche, K., Geist, R., and Westall, J. 2005.
  Detail preserving reproduction of color images
  for monochromats and dichromats. IEEE Comput.
  Graph. Appl. 25, 3.
• http//www.google.com
• http//www.en.wikipedia.org/wiki/Lab_color_space
• http//scien.stanford.edu/class/psych221/projects/
  98/ciecam/Description.html
• http//scidok.sulb.uni-saarland.de/volltexte/2007/
  1201/pdf/Dissertation_37_Mant_Rafa_2006.pdf
• And several more pages talking about this topic

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TU Berlin
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THANK YOU
Color
THANK YOU
Color2Gray
Imanol Gómez Rubio Computational Photography
TU Berlin