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Color transfer between high-dynamic-range images

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Color transfer methods alter the look of a source image with regards to a reference image. So far, the proposed color transfer methods have been limited to low-dynamic-range (LDR) images. Unlike LDR images, which are display-dependent, high-dynamic-range (HDR) images contain real physical values of the world luminance and are able to capture high luminance variations and finest details of real world scenes. Therefore, there exists a strong discrepancy between the two types of images. In this paper, we bridge the gap between the color transfer domain and the HDR imagery by introducing HDR extensions to LDR color transfer methods. We tackle the main issues of applying a color transfer between two HDR images. First, to address the nature of light and color distributions in the context of HDR imagery, we carry out modifications of traditional color spaces. Furthermore, we ensure high precision in the quantization of the dynamic range for histogram computations. As image clustering (based on light and colors) proved to be an important aspect of color transfer, we analyze it and adapt it to the HDR domain. Our framework has been applied to several state-of-the-art color transfer methods. Qualitative experiments have shown that results obtained with the proposed adaptation approach exhibit less artifacts and are visually more pleasing than results obtained when straightforwardly applying existing color transfer methods to HDR images.

Publié dans : Ingénierie
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Color transfer between high-dynamic-range images

  1. 1. Color transfer between high- dynamic-range images H. Hristova, R. Cozot, O. Le Meur, K. Bouatouch University of Rennes 1 Rennes, France
  2. 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. 3. Main goal ● Carrying out a color transfer between two HDR images directly in the HDR domain Input Reference 3 ● Solution: apply color transfer methods to stylize an HDR image with regards to a reference image
  4. 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. 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. 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. 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. 8. 8 Extension to HDR images • Linear search for significant peaks in the image hue histogram - Colors-based style images: more than one significant color cluster - Light-based style images: one significant color cluster Input and reference images Color space conversion Image classification Clustering and mapping Color transfer Chromatic adaptation transform Final result [Hristovaetal.,2015] • The number of significant peaks determines the number of clusters - Colors-based style images: hue histogram - Light-based style images: luminance histogram
  9. 9. Extension to HDR images Input and reference images Color space conversion Image classification Clustering and mapping Color transfer Chromatic adaptation transform Final result 9 [Hristovaetal.,2015] LDR images CIE Lab L channel of CIE Lab HDR images hdr-CIELab Log- luminance ● 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] ModificationsModified
  10. 10. Extension to HDR images 10 LDR images CIE Lab L channel of CIE Lab L-based clustering HDR images hdr-CIELab Log- luminance Log- luminance clustering [Hristovaetal.,2015] Logarithmic transform Input and reference images Color space conversion Image classification Clustering and mapping Color transfer Chromatic adaptation transform Final result ModifiedModifications
  11. 11. Extension to HDR images 11 Local CAT Cluster- based local CAT [Hristovaetal.,2015] LDR images CIE Lab L channel of CIE Lab L-based clustering HDR images hdr-CIELab Log- luminance Log- luminance clustering Input and reference images Color space conversion Clustering and mapping Color transfer Chromatic adaptation transform Final result Image classification ModifiedModifications
  12. 12. Extension to HDR images 12 Gaussianlow-passfilter (h) (m) (sh) (sh) (m) (h) Local CAT Cluster- based local CAT [Hristovaetal.,2015] LDR images CIE Lab L channel of CIE Lab L-based clustering HDR images hdr-CIELab Log- luminance Log- luminance clustering Input and reference images Color space conversion Clustering and mapping Color transfer Chromatic adaptation transform Final result Image classification ModifiedModifications
  13. 13. Extension to HDR images 13 Input ReferenceCluster-based local CAT Local CAT Cluster- based local CAT [Hristovaetal.,2015] LDR images CIE Lab L channel of CIE Lab L-based clustering HDR images hdr-CIELab Log- luminance Log- luminance clustering Input and reference images Color space conversion Clustering and mapping Color transfer Chromatic adaptation transform Final result Image classification ModifiedModifications
  14. 14. Extension to HDR images 14 Input ReferenceCluster-based local CAT Local CAT Cluster- based local CAT [Hristovaetal.,2015] LDR images CIE Lab L channel of CIE Lab L-based clustering HDR images hdr-CIELab Log- luminance Log- luminance clustering Input and reference images Color space conversion Clustering and mapping Color transfer Chromatic adaptation transform Final result Image classification ModifiedModifications
  15. 15. Objective evaluation of the results 15 ● 10 image pairs ● Two tone-mapping operators: [Durand et al., 2002] and [Reinhard et al., 2002] ● SSIM and Bhattacharya coefficient
  16. 16. Results 16 Input Reference Color transfer with CAT Color transfer without CAT Color transfer with CAT Color transfer without CAT [Hristova et al., 2015] HDR extension
  17. 17. Generalization and results 17 [Reinhard et al., 2001] - global method [Tai et al., 2005] - clustering (local transformations) Input Reference
  18. 18. Generalization and results 18 [Pitié et al., 2007] - CIE Lab [Pitié et al., 2007] - hdr-CIELab Input Reference
  19. 19. Generalization and results 19 [Bonneel et al., 2013] - luminance clustering [Bonneel et al., 2013] - log-luminance clustering Input Reference
  20. 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 - Need for better HDR color spaces 20
  21. 21. Thank you for your attention! 21

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