1. Color Transfer in Correlated Color Space Xuezhong Xiao, Computer Science & Engineering Department, Shanghai Jiao Tong University Lizhuang Ma., Computer Science & Engineering Department, Shanghai Jiao Tong University Computer vision and Pattern Recognition 2006

2. Introduction Conveying the color characteristic of a warm-tone photograph (a) to a cold-tone one (b) in RGB color space.

3. Introduction Recently there were several important works about color alteration introduced to computer graphics community, including color transfer [ Reinhard et al. 2001] and colorization [Welsh et al. 2002][Levin et al. 2004][Ironi et al. 2005] and its reverse processing [Gooch et al. 2005]. Our approach alters target image's color through a series of transformations that include translation, scaling and rotation, derived from these statistics- mean and covariance of the target and source images.

4. Color transfer between images - [Reinhard et al. 2001] RGBlab RGB

5. Transferring color to greyscale images- [Welsh et al. 2002]

6. Colorization using optimization - [Levin et al. 2004]

7. Colorization by example - [Ironi et al. 2005]

8. Color2Gray: salience-preserving color removal- [Gooch et al. 2005]

9. Statistics-based Method We develop a statistics-based method that can achieve the goal in RGB color space just utilizing the statistics-mean and covariance matrix. The essence of our method is moving data points of source image by scaling, rotation and translating to fit data points‘ cluster of target image in RGB color space.

10. Statistics-based Method First, we calculate the mean of pixel data along the three axes and the covariance matrix between the three components in color space for both the source and target images. All of the statistics are denoted by (R src, G src, B src ), (R tgt, G tgt, B tgt ), Cov src and Cov tgt, respectively.

11. Statistics-based Method We decompose the covariance matrices using SVD algorithm, namely, Cov =U ‧ Λ ‧ V T –U and V are orthogonal matrices and are composed of the eigenvectors of Cov –Λ=diag( λ R, λ G, λ B ) –λ R, λ G, λ B are the eigenvalues of Cov –U is a rotation matrix to manipulate pixels of the source image in the next step

12. Statistics-based Method Last, we do a transformation as follows: I = T src ‧ R src ‧ S src ‧ S tgt ‧ R tgt ‧ T tgt ‧ I tgt –I = (R, G, B, 1) T –I tgt = (R tgt, G tgt, B tgt, 1) T –T src, T tgt, R src, R tgt, S src and S tgt denote the matrices of translation, rotation and scaling

13. Statistics-based Method

16. The mean is the center coordinates of an ellipsoid, and the eigenvalues and eigenvectors of the covariance matrix indicate the length and orientation of the three axes of the ellipsoid.

17. Statistics-based Method

19. Swatch-based Transfer When we transfer one image's appearance to another, it's possible to select source and target images that don't work well together. The result's quality depends on the images' similarity in composition.

20. Swatch-based Transfer

21. We formulate Reinhard’s approach and provide some parameters for more flexible user interaction. 1.select separate swatches from the source and target images 2.calculate swatches' statistics-the mean and covariance matrices and transfer target image for the different swatch pairs 3.compute the distances from each pixel in the intermediate images to the center of each fitting ellipsoid of target swatch data.

22. Swatch-based Transfer 4.We blend the pixels with same coordinates in separate result images with weights inversely proportional to these distances, yielding the final color. –I = (R, G, B) T is the final result's color –w ij are the weights according to the distances between pixel data and the center of swatches –I j = (R j, G j, B j ) T are the intermediate images' data corresponding to separate target swatches –m and n are the numbers of swatches in the source and target image –a i is the ratio of the corresponding swatch's area in the whole

23. Results