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Color2Gray: Salience-Preserving Color Removal

Color2Gray: Salience-Preserving Color Removal. Amy Gooch Sven Olsen Jack Tumblin Bruce Gooch. Grayscale. Color. New Algorithm. Problem. Volume of displayable CIE L*a*b* Colors. Isoluminant Colors. Color. Grayscale. Converting to Grayscale…. In Color Space Linear Nonlinear

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Color2Gray: Salience-Preserving Color Removal

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  1. Color2Gray: Salience-Preserving Color Removal Amy Gooch Sven Olsen Jack Tumblin Bruce Gooch

  2. Grayscale Color New Algorithm

  3. Problem Volume of displayable CIE L*a*b* Colors

  4. Isoluminant Colors Color Grayscale

  5. 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…..

  6. Traditional Methods: Luminance Channels Problem can not be solved by simply switching to a different space CIE CAM 97 Photoshop LAB CIE XYZ YCrCb

  7. Photoshop Grayscale Traditional Methods • Contrast enhancement & Gamma Correction • Doesn’t help with isoluminant values PSGray + Auto Contrast New Algorithm

  8. Simple Linear Mapping Luminance Axis

  9. Luminance Axis Principal Component Analysis (PCA)

  10. Problem with PCA Worst case: Isoluminant Colorwheel

  11. Non-linear mapping

  12. Contemporaneous Research • Rasche et al. [2005, IEEE CG&A and EG] Color Image Luminance Only Rasche et al.'s Method

  13. Goals • Dimensionality Reduction • From tristimulus values to single channel Loss of information • Maintain salient features in color image • Human perception

  14. Relative differences Color Illusion by Lotto and Purves http://www.lottolab.org

  15. Challenge 1:Influence of neighboring pixels

  16. Challenge 2:Dimension and Size Reduction 120, 120 100 0 -120, -120

  17. Challenge 3:Many Color2Gray Solutions Original . . .

  18. color2gray For first pixel L1 + e1,2 L2 1 2 L1 + e1,2 L2 + e2,1 For Nth pixel L2 + e2,1 L1 L1 L1 L1 L2 L2 L2 Algorithm Intuition Look at DC . . . i = 1, j = 2 luminance Look at DC

  19. Algorithm Overview • Convert to Perceptually Uniform Space • CIE L*a*b* • Adjust g to incorporate both luminance and chrominance differences • Initialize image, g, with L channel • For every pixel • Compute Luminance distance • Compute Chrominance distance dij

  20. Color2Grey Algorithm Optimization: minSS ( (gi - gj) - di,j)2 i+m i j=i-m

  21. Parameters Map chromatic difference to increases or decreases in luminance values m : Radius of neighboring pixels a : Max chrominance offset q :

  22. m : Neighborhood Size • = 300o a = 10 m = 2 m = 16 • = entire image • = 49o a = 10

  23. m : Neighborhood Size m = 16 • = entire image

  24. a: Chromatic variation maps to luminance variation a -a crunch(x) = a * tanh(x/a) a = 5 a = 10 a = 25

  25. Perceptual Distance DLij = Li - Lj • Luminance Distance: • Chrominance Distance: ||DCij|| Problem: ||DCij|| is unsigned

  26. Map chromatic difference to increases or decreases in luminance values +b* C2 -a* +a* Color Space C1

  27. +Db* Color Difference Space +DC1,2 vq= (cos q, sin q) + - vq q -Da* +Da* + - sign(||DCi,j||) = sign(DCi,j .vq ) -Db*

  28. q = 45 q = 225 Photoshop Grayscale

  29. q = 135 q = 45 q = 0 Grayscale

  30. How to CombineChrominance and Luminance dij = DLij (Luminance)

  31. How to CombineChrominance and Luminance (Luminance) if |DLij| > ||DCij|| DLij dij = ||DCij|| (Chrominance)

  32. 120, 120 100 a -a crunch(x) = a * tanh(x/a) 0 -120, -120 How to CombineChrominance and Luminance DLij if |DLij| > crunch(||DCij||) d (a)ij = crunch(||DCij||)

  33. Grayscale . . . How to CombineChrominance and Luminance DLij if |DLij| > crunch(||DCij||) d(a,q)ij = if DCij . nq ≥ 0 crunch(||DCij||) otherwise crunch(-||DCij||)

  34. Color2Grey Algorithm Optimization: minSS ( (gi - gj) - di,j)2 i+m i j=i-m If dij == DL then ideal image is g Otherwise, selectively modulated by DCij

  35. Color2Grey + Color Results Photoshop Grey Original Color2Grey

  36. Original PhotoshopGrey Color2Grey Color2Grey+Color

  37. Original PhotoshopGrey Color2Grey

  38. Original PhotoshopGrey Color2Grey

  39. Implementation Performance • Image of size S x S • O(m2 S2) or O(S4) for full neighborhood case • 12.7s 100x100 image • 65.6s 150x150 image • 204.0s 200x200 image • GPU implementation • O(S2) ideal, really O(S3) • 2.8s 100x100 • 9.7s 150x150 • 25.7s 200x200 Athlon 64 3200 CPU NVIDIA GeForce GT6800

  40. Future Work • Faster • Multiscale • Animations/Video • Smarter • Remove need to specify q • New optimization function designed to match both signed and unsigned difference terms • Image complexity measures

  41. Validate "Salience Preserving" Original PhotoshopGrey Color2Grey Apply Contrast Attention model by Ma and Zhang 2003

  42. Validate "Salience Preserving" Original PhotoshopGrey Color2Grey

  43. Thank you www.color2gray.info • SIGGRAPH Reviewers • NSF • Helen and Robert J. Piros Fellowship • Northwestern Graphics Group • MidGraph2004 Participants • especially Feng Liu • (sorry I spelled your name wrong in the acknowledgements)

  44. Original Color2Grey Color2Grey+Color

  45. Original Color2Grey Color2Grey+Color

  46. Color2Grey Original Color2Grey+Color

  47. Original PhotoshopGrey Color2Grey

  48. Original Color2Grey Color2Grey+Color

  49. Original PhotoshopGrey Color2Grey

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