Color Transfer Between High Dynamic Range Images: Extension to HDR Domain

1. Color transfer between high-dynamic-range images H. Hristova, R. Cozot, O. Le Meur, K. Bouatouch University of Rennes 1 Rennes, France

2. Outline • Introduction • Main objective • Contributions • Extension to the HDR domain of a color transfer method • Results and evaluation • Generalization for state-of-the-art color transfer methods • Conclusion 2

3. Main goal • Carrying out a color transfer between two HDR images directly in the HDR domain Input Reference • Solution: apply color transfer methods to stylize an HDR image with regards to a reference image 3

4. Why do LDR color transfer methods need to be extended to the HDR domain? • LDR color spaces • - well predict the color gamut for luminance levels between zero and the display white point • - uncertain applicability to HDR images • Color trend above the perfect diffuse white 4

5. Why do LDR color transfer methods need to be extended to the HDR domain? • Assumption: a unique multivariate Gaussian distribution • HDR domain: to fit the high range of lightness of HDR images we need to assume mixture of Gaussian distributions 5

6. Why do LDR color transfer methods need to be extended to the HDR domain? • Lightness - approximated by luminance in the LDR domain • HDR domain - distinguish between the absolute luminance and the lightness (the L channel of CIE Lab) 6

7. Contributions • Adaptation of [Hristova et al., 2015] color transfer method to HDR images • - HDR color spaces • - Modifications of the clustering step and of the image classification • Cluster-based local chromatic adaptation transform • Generalization for state-of-the-art color transfer methods 7

8. Extension to HDR images Input and reference images Color space conversion Image classification Clustering and mapping Color transfer Chromatic adaptation transform Final result [Hristova et al., 2015] • Linear search for significant peaks in the image hue histogram • The number of significant peaks determines the cluster number • - Colors-based style images: hue histogram • - Light-based style images: luminance histogram 8

9. Extension to HDR images LDR images CIE Lab L channel of CIE Lab Input and reference images Color space conversion Image classification Clustering and mapping Color transfer Chromatic adaptation transform Final result [Hristova et al., 2015] Log- luminance HDR images hdr-CIELab • Dashed line: cubic function of L channel (CIE Lab) • Solid line: Michaelis-Menten function by which we replace the cubic function of L channel (CIE Lab) • hdr-CIELab color space [Fairchild et al., 2004] [Fairchild et al., 2004] 9

10. Extension to HDR images LDR images CIE Lab L channel of CIE Lab L-based clustering Input and reference images Color space conversion Image classification Clustering and mapping Color transfer Chromatic adaptation transform Final result [Hristova et al., 2015] Log- luminance HDR images hdr-CIELab Log- luminance clustering Logarithmic transform 10

11. Extension to HDR images LDR images CIE Lab L channel of CIE Lab L-based clustering Local CAT Input and reference images Color space conversion Image classification Clustering and mapping Color transfer Chromatic adaptation transform Final result [Hristova et al., 2015] Log- luminance Cluster- based local CAT HDR images hdr-CIELab Log- luminance clustering 11

12. Extension to HDR images LDR images CIE Lab L channel of CIE Lab L-based clustering Local CAT Input and reference images Color space conversion Image classification Clustering and mapping Color transfer Chromatic adaptation transform Final result [Hristova et al., 2015] Log- luminance Cluster- based local CAT HDR images hdr-CIELab Log- luminance clustering (h) (sh) (m) (h) (m) Gaussian low-pass filter (sh) 12

13. Extension to HDR images LDR images CIE Lab L channel of CIE Lab L-based clustering Local CAT Input and reference images Color space conversion Image classification Clustering and mapping Color transfer Chromatic adaptation transform Final result [Hristova et al., 2015] Log- luminance Cluster- based local CAT HDR images hdr-CIELab Log- luminance clustering Input Cluster-based local CAT Reference 13

14. Extension to HDR images LDR images CIE Lab L channel of CIE Lab L-based clustering Local CAT Input and reference images Color space conversion Image classification Clustering and mapping Color transfer Chromatic adaptation transform Final result [Hristova et al., 2015] Log- luminance Cluster- based local CAT HDR images hdr-CIELab Log- luminance clustering Input Cluster-based local CAT Reference 14

15. Objective evaluation of the results • 10 image pairs • Two tone-mapping operators: [Durand et al., 2002] and [Reinhard et al., 2002] • SSIM and Bhattacharya coefficient 15

16. Results [Hristova et al., 2015] HDR extension Color transfer with CAT Input Color transfer with CAT Reference Color transfer without CAT Color transfer without CAT 16

17. Generalization and results Input Reference [Tai et al., 2005] - clustering (local transformations) [Reinhard et al., 2001] - global method 17

18. Generalization and results Input Reference [Pitié et al., 2007] - CIE Lab [Pitié et al., 2007] - hdr-CIELab 18

19. Generalization and results Input Reference [Bonneel et al., 2013] - log-luminance clustering [Bonneel et al., 2013] - luminance clustering 19

20. Conclusion • Extension of a novel local color transfer method [Hristova et al., 2015] • Modifications to CIE Lab -> hdr-CIELab • Luminance/Lightness -> Log-luminance • Generalization to state-of-the-art methods • Future work • Need for a more precise color mapping/color transformation between two HDR images • HDR color spaces 20

21. Thank you for your attention! 21