1. CAGD&C G Color Transfer between Images Reporter:Chen Shuangmin

2. CAGD&C G Introduction to the authors Erik Reinhard.is a postdoctoral fellow at the University of Utah.interested in visual-perception and parllelgraphics fields..BS diploma in computer science from Deft University of Technology and a phD in computer science from the university of Bristol

3. CAGD&C G Introduction to the authors Bruce Gooch.is a graduate student at the university of Utah.intersted in nonphotorealistic rendering.BS in mathematics and an MS in computer science from the university of Utah

4. CAGD&C G Introduction to the authors Michael Ashikhmin.an assistant professor at State University of New York.intersted in developing simple and practical algorithms for computer graphics.has an MS in physics from the Moscow Institute of Physics and Technology,an MS in chemistry from the University of California

5. CAGD&C G Guidence Color correction(remove undesired color- illumination) - in one image Red eye restoration

6. CAGD&C G Guidence Color correction(remove undesired color- illumination) Borrow color from one image to another Similar composition sourcetarget

7. CAGD&C G Main idea 1.Color constancy correction.global statistics.swatches 2.Gamma correction(logx r = rlogx) Goal Alter an image’s color

8. CAGD&C G 1.Color constancy correction Why choose lab color space???.RGB.lab.CIECAM97s Why decorrelation is better??? --- Gloabal statistics

9. CAGD&C G Introduction to RGB We can get any color by blending with RGB Definition with cubic

10. CAGD&C G Introduction to lab color space In Photoshop,the range of l is 0 to 100;and in color palete,the ranges of a and b are both 120 to -120. It will be more easily understood in Gamma correction

11. CAGD&C G Why choose lab color space? 2000 random points --decorrelation

12. CAGD&C G Why decorrelation is better? Apply different operations in different color channels that without undesirable crosschannel artifacts. The data in lab space is more compressed (log space) RGB:correlations between the different channel’s values. (0,0,0) - (255,255,255)

13. CAGD&C G Comparison of the effects In three color spaces Source image Target image

14. CAGD&C G Comparison of effects In three color spaces RGB color space Lab color space CIECAM97s

15. CAGD&C G Convertion between color spaces RGB lab.RGB XYZ (international standard).XYZ LMS.LMS LMS.LMS lab lab RGB

16. CAGD&C G About CIECAM97s RGB XYZ (international standard) XYZ LMS

17. CAGD&C G Convertion between color spaces RGB lab.RGB XYZ.XYZ LMS

18. CAGD&C G Convertion between color spaces RGB lab.LMS LMS.LMS lab

19. CAGD&C G Convertion between color spaces RGB lab lab RGB(inverse operations) L = 10 L M = 10 M S = 10 S

20. CAGD&C G Main idea 1.Color constancy correction.global statistics.swatches 2.Gamma correction(logx r = rlogx)

21. CAGD&C G Two concept in statistics Standard deviation Mean value Show with chalk on blackboard

22. CAGD&C G Color constancy correction Subtract the mean from the data points Scale the data points comprising the synthetic image by factors determined by the respective standard deviation

23. CAGD&C G Color constancy correction add the averages computed for the photagraph. Displaying: convert the result back to RGB via logLMS,LMS,and XYZ color spaces using equations +

24. CAGD&C G Convert back to RGB for displaying RGB lab lab RGB(inverse operations) L = 10 L M = 10 M S = 10 S

25. CAGD&C G Main idea 1.Color constancy correction.global statistics.swatches 2.Gamma correction(logx r = rlogx)

26. CAGD&C G Swatches When source and target images don’t work well together – swatches.

27. CAGD&C G Swatches Several pairs of clusters in lab space (cluster for each swatch) -Compute statistics for each swatch

28. CAGD&C G Swatches -result When source and target images don’t work well – swatches. result targetsource

29. CAGD&C G Main idea 1.Color constancy correction.global statistics.swatches 2.Gamma correction(logx r = r logx)

30. CAGD&C G My question about this paper I don’t know how to decide which is source image and which is target image. It will give different effects. result I don’t like this result! result This effect is much better! I like it. T S T S

31. CAGD&C G My question about this paper I don’t know which two images will be matching better.How to choose? result This effect is much better! I like it.(result of this paper.) result This effect is not much better!

32. CAGD&C G My question about this paper The size of these two images is related to the result. Target 454*366 Source 426*266 Target 800*600 Source 426*266

33. CAGD&C G Thank you!