1. Color2Gray Imanol Gómez Rubio Computational Photography – 11/Dec/2007 TU-Berlin

2. 2 Index 1. Introduction 1. Introduction 2. Converting to Grayscale 2. Converting to Grayscale 3. Color2Gray 3. Color2Gray 3.1 Challenges3.1 Challenges 3.2 Algorithm3.2 Algorithm 4. Future Work 4. Future Work 5. Examples 5. Examples 6. references 6. references Imanol Gómez Rubio – Computational Photography – TU Berlin

3. 3 1. Introduction Imanol Gómez Rubio – Computational Photography – TU Berlin Color New Algorithm Grayscale ‘Impressionist Sunrise’ by Claude Monet

4. 4 1. Int: Isoluminant Colors Imanol Gómez Rubio – Computational Photography – TU Berlin ColorGrayscale

5. 5 1. Int: Featureless Conversion Imanol Gómez Rubio – Computational Photography – TU Berlin ColorGrayscaleColor2Gray

6. 6 2. Converting to Grayscale Imanol Gómez Rubio – Computational Photography – TU Berlin 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…..

7. 7 2. Conv: In ColorSpace Imanol Gómez Rubio – Computational Photography – TU Berlin Color Simple linear mapping Non linear mapping

8. 8 2. Conv: Related Work Previous Methods from Color2gray Previous Methods from Color2gray Based on changing to CIE L*a*bBased on changing to CIE L*a*b Imanol Gómez Rubio – Computational Photography – TU Berlin

9. 9 2. Conv: Iluminance channels Imanol Gómez Rubio – Computational Photography – TU Berlin CIE CAM 97Photoshop LAB CIE XYZYCrCb Problem can not be solved by simply switching to a different space

10. 10 This Method attempts to preserve the salient features of the color image and the relative differences. This Method attempts to preserve the salient features of the color image and the relative differences. Human PerceptionHuman Perception Imanol Gómez Rubio – Computational Photography – TU Berlin 3. Color2Gray

11. 11 3.1 Challenges: 1 Influence of Neighboring pixels Influence of Neighboring pixels Imanol Gómez Rubio – Computational Photography – TU Berlin

12. 12 3.1 Challenges: 2 Dimension and size reduction Dimension and size reduction Imanol Gómez Rubio – Computational Photography – TU Berlin -120, -120 120, 120 0 100

13. 13 3.1 Challenges: 3 Many Color2Gray Solutions Many Color2Gray Solutions Imanol Gómez Rubio – Computational Photography – TU Berlin Original......

14. 14 3.2 Algorithm: Overview Convert to Perceptually Uniform Space Convert to Perceptually Uniform Space CIE L*a*b*CIE L*a*b* Initialize image, g, with L channel Initialize image, g, with L channel For every pixel For every pixel Compute Luminance distanceCompute Luminance distance Compute Chrominance distanceCompute Chrominance distance Adjust g to incorporate both luminance and chrominance differences Adjust g to incorporate both luminance and chrominance differences Imanol Gómez Rubio – Computational Photography – TU Berlin  ij

15. 15 3.2 Algorithm: Parameters Imanol Gómez Rubio – Computational Photography – TU Berlin Radius of neighboring pixels  Max chrominance offset  Map chromatic difference to increases or decreases in luminance values 

16. 16 3.2 Algorithm: 3.2 Algorithm:  Imanol Gómez Rubio – Computational Photography – TU Berlin Color  = 9 Full Neighborhood

17. 17 3.2 Algorithm: 3.2 Algorithm:  Imanol Gómez Rubio – Computational Photography – TU Berlin  = 5  = 15  = 25  = 35  = 45  = 55  = 65  = 75  = 85  = 95

18. 18 3.2 Algorithm: 3.2 Algorithm:  Imanol Gómez Rubio – Computational Photography – TU Berlin

19. 19 3.2 Algorithm: combining Chrominance and Luminance Imanol Gómez Rubio – Computational Photography – TU Berlin  ij   L ij ||  C ij || (Luminance) if |  L ij | > ||  C ij || ( Chrominance )‏

20. 20 3.2 Algorithm: combining Chrominance and Luminance Imanol Gómez Rubio – Computational Photography – TU Berlin  ij   L ij crunch(||  C ij ||)‏ if |  L ij | > crunch(||  C ij ||)‏   crunch(x) =  * tanh(x/  )‏ -120, -120 120, 120 0 100

21. 21 3.2 Algorithm: combining Chrominance and Luminance Imanol Gómez Rubio – Computational Photography – TU Berlin......  ij   L ij crunch(||  C ij ||)‏ if  C ij.  ≥ 0 if |  L ij | > crunch(||  C ij ||)‏ crunch(-||  C ij ||)‏ otherwise Grayscale v  = (cos , sin  )‏

22. 22 3.2 Algorithm: Optimization Imanol Gómez Rubio – Computational Photography – TU Berlin min   ( (g i - g j ) -  i,j   i j=i-  i+ 

23. 23 4. Future Work Imanol Gómez Rubio – Computational Photography – TU Berlin Animations/Video Animations/Video Faster Multiscale Smarter Remove need to specify  New optimization function designed to match both signed and unsigned difference terms Image complexity measures

24. 24 5. Examples Imanol Gómez Rubio – Computational Photography – TU Berlin ColorPhotoshopColor2gray

25. 25 5. Examples Imanol Gómez Rubio – Computational Photography – TU Berlin Color Photoshop Color2gray

26. 26 5. Examples Imanol Gómez Rubio – Computational Photography – TU Berlin Color Photoshop Color2gray

27. 27 6. References Imanol Gómez Rubio – Computational Photography – TU Berlin http://www.color2gray.info Volk, C., 2000. Adobe Photoshop Tip of the Week Tutorial. http://www.carlvolk.com/photoshop21.htm.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://www.en.wikipedia.org 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 http://scidok.sulb.uni- saarland.de/volltexte/2007/1201/pdf/Dissertation_37_Mant_Rafa_2006.pdf And several more pages talking about this topic

28. 28 Imanol Gómez Rubio – Computational Photography – TU Berlin THANK YOU Color THANK YOU Color2Gray