Enhancing Color Representation for the Color Vision Impaired (CVAVI 2008)
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The document discusses a proposed algorithm to enhance color representation for individuals with color vision deficiency (CVD) using generalized histogram equalization and controlling the enhancement degree. It outlines the challenges and existing methods related to color perception in CVD, as well as presents interactive applications and results from the approach. The conclusion emphasizes a fast re-coloring algorithm that could improve web accessibility and assistive technologies for designers.
Enhancing Color Representation for the Color Vision Impaired (CVAVI 2008)
- 1. Enhancing Color Representation for the Color Vision Impaired Jia-Bin Huang1, Sih-Ying Wu2, and Chu-Song Chen1 [email protected] 1 Institute of Information Science Academia Sinica 2 Department of Electronics Engineering National Chiao Tung University October 18, 2008 Jia-Bin Huang et al. (IIS, Academia Sinica) Enhancing Color Representation for the Color Vision Impaired
- 2. Outline 1 Introduction Color Vision Deficiency (CVD) Related Works 2 The Proposed Algorithm Main Idea Generalized Histogram Equalization Controlling the Enhancement Degree 3 Results and Discussion Visual Results Interactive Interface Applications 4 Conclusion Jia-Bin Huang et al. (IIS, Academia Sinica) Enhancing Color Representation for the Color Vision Impaired
- 3. Outline 1 Introduction Color Vision Deficiency (CVD) Related Works 2 The Proposed Algorithm Main Idea Generalized Histogram Equalization Controlling the Enhancement Degree 3 Results and Discussion Visual Results Interactive Interface Applications 4 Conclusion Jia-Bin Huang et al. (IIS, Academia Sinica) Enhancing Color Representation for the Color Vision Impaired
- 4. Color Vision Normal vision Three different fundamental photoreceptor cells (cone cells) Peak responses lie in long (L), middle (M), and short (S) wavelength regions Jia-Bin Huang et al. (IIS, Academia Sinica) Enhancing Color Representation for the Color Vision Impaired
- 5. Classification of CVD Anomalous trichromacy One of the three cone cells is abnormal Dichromacy One of the three cone cells is absent Monochromacy All three cone cells are absent Table: Major genetic color deficiencies. Type Name Cause of defect Protanomaly L-cone defect Anomalous trichromacy Deuteranomaly M-cone defect Tritanomaly S-cone defect Protanopia L-cone absent Dichromacy Deuteranopia M-cone absent Tritanopia S-cone absent Monochromacy Rod Monochromacy no functioning cones Jia-Bin Huang et al. (IIS, Academia Sinica) Enhancing Color Representation for the Color Vision Impaired
- 6. How People with CVD perceive Colors? Anomalous trichromacy Protanomaly Deuteranomaly Tritanomaly Dichromacy Protanopia Deuteranopia Tritanopia Jia-Bin Huang et al. (IIS, Academia Sinica) Enhancing Color Representation for the Color Vision Impaired
- 7. Related Works 1 Guideline or assistant tool for designers to avoid ambiguous color combinations Provide guideline (Chisholm et al. 01) Use a restricted CVD palette (Rigden et al. 99, Vienot99 et al. 99) Verify color schemes (Walraven et al.97, Jenny07 et al. 07) 2 (Semi-)automatically reproduce colors that are suitable for CVD viewers Jia-Bin Huang et al. (IIS, Academia Sinica) Enhancing Color Representation for the Color Vision Impaired
- 8. Related Works 1 Guideline or assistant tool for designers to avoid ambiguous color combinations Provide guideline (Chisholm et al. 01) Use a restricted CVD palette (Rigden et al. 99, Vienot99 et al. 99) Verify color schemes (Walraven et al.97, Jenny07 et al. 07) 2 (Semi-)automatically reproduce colors that are suitable for CVD viewers Jia-Bin Huang et al. (IIS, Academia Sinica) Enhancing Color Representation for the Color Vision Impaired
- 9. Related Works 1 Guideline or assistant tool for designers to avoid ambiguous color combinations Provide guideline (Chisholm et al. 01) Use a restricted CVD palette (Rigden et al. 99, Vienot99 et al. 99) Verify color schemes (Walraven et al.97, Jenny07 et al. 07) 2 (Semi-)automatically reproduce colors that are suitable for CVD viewers Jia-Bin Huang et al. (IIS, Academia Sinica) Enhancing Color Representation for the Color Vision Impaired
- 10. Typical Re-coloring Approaches Common procedures Select key colors using color quantization Find the optimal mapping of key colors using optimization procedure Obtain the re-colored image through interpolation (Ichikawa et al. 04, Wakita et al. 05, Rasche et al. 05, JeRerson et al. 06, Huang et al. 07) Drawbacks: Slow. Require a few minutes for an image Problem of gamut mapping, illumination inconsistency. Unnatural color for the colorblind Jia-Bin Huang et al. (IIS, Academia Sinica) Enhancing Color Representation for the Color Vision Impaired
- 11. Typical Re-coloring Approaches Common procedures Select key colors using color quantization Find the optimal mapping of key colors using optimization procedure Obtain the re-colored image through interpolation (Ichikawa et al. 04, Wakita et al. 05, Rasche et al. 05, JeRerson et al. 06, Huang et al. 07) Drawbacks: Slow. Require a few minutes for an image Problem of gamut mapping, illumination inconsistency. Unnatural color for the colorblind Jia-Bin Huang et al. (IIS, Academia Sinica) Enhancing Color Representation for the Color Vision Impaired
- 12. Typical Re-coloring Approaches Common procedures Select key colors using color quantization Find the optimal mapping of key colors using optimization procedure Obtain the re-colored image through interpolation (Ichikawa et al. 04, Wakita et al. 05, Rasche et al. 05, JeRerson et al. 06, Huang et al. 07) Drawbacks: Slow. Require a few minutes for an image Problem of gamut mapping, illumination inconsistency. Unnatural color for the colorblind Jia-Bin Huang et al. (IIS, Academia Sinica) Enhancing Color Representation for the Color Vision Impaired
- 13. Typical Re-coloring Approaches Common procedures Select key colors using color quantization Find the optimal mapping of key colors using optimization procedure Obtain the re-colored image through interpolation (Ichikawa et al. 04, Wakita et al. 05, Rasche et al. 05, JeRerson et al. 06, Huang et al. 07) Drawbacks: Slow. Require a few minutes for an image Problem of gamut mapping, illumination inconsistency. Unnatural color for the colorblind Jia-Bin Huang et al. (IIS, Academia Sinica) Enhancing Color Representation for the Color Vision Impaired
- 14. Outline 1 Introduction Color Vision Deficiency (CVD) Related Works 2 The Proposed Algorithm Main Idea Generalized Histogram Equalization Controlling the Enhancement Degree 3 Results and Discussion Visual Results Interactive Interface Applications 4 Conclusion Jia-Bin Huang et al. (IIS, Academia Sinica) Enhancing Color Representation for the Color Vision Impaired
- 15. Main Idea Main Idea Maintain luminance and saturation consistency. =>Work in the HSV color space, and leave value and saturation component unchanged Preserve the natural order of hue. =>Apply a hue transfer function to hue channel (non-decreasing) Jia-Bin Huang et al. (IIS, Academia Sinica) Enhancing Color Representation for the Color Vision Impaired
- 16. Contrast Enhancement using Histogram Equalization (HE) One of the most well-known technique for image enhancement Assign larger dynamic range for intensity values having larger number of occurrences g T (g) = gmin + (gmax − gmin) hist(g) dg gmin T (g): Intensity mapping function hist(g): Normalized histogram (probability distribution of the grey levels in the image) gmax, gmin: Maximum and minimum intensity values Jia-Bin Huang et al. (IIS, Academia Sinica) Enhancing Color Representation for the Color Vision Impaired
- 17. Histogram Generalization Original Histogram Histogram generation Masking-and-accumulating using 1 × 1 block Generalized Histogram Generalized histogram Extend 1 × 1 block to N × N to encode spatial information Replace sampling by measurement(feature extraction) Jia-Bin Huang et al. (IIS, Academia Sinica) Enhancing Color Representation for the Color Vision Impaired
- 18. Three Local Measurement 1 Hue value at point (x, y) α(x, y) = hue(x, y) 2 Local hue difference β(x, y) = max{hue(i, j)} − min{hue(i, j)}, (i, j) ∈ N(x, y) i,j i,j 3 Color confusability due to CVD γ(x, y) = (||(C(x, y) − C(i, j))||− (i,j)∈N(x,y) ||(Sim(C(x, y)) − Sim(C(i, j)))||)2 Jia-Bin Huang et al. (IIS, Academia Sinica) Enhancing Color Representation for the Color Vision Impaired
- 19. Three Local Measurement 1 Hue value at point (x, y) α(x, y) = hue(x, y) 2 Local hue difference β(x, y) = max{hue(i, j)} − min{hue(i, j)}, (i, j) ∈ N(x, y) i,j i,j 3 Color confusability due to CVD γ(x, y) = (||(C(x, y) − C(i, j))||− (i,j)∈N(x,y) ||(Sim(C(x, y)) − Sim(C(i, j)))||)2 Jia-Bin Huang et al. (IIS, Academia Sinica) Enhancing Color Representation for the Color Vision Impaired
- 20. Three Local Measurement 1 Hue value at point (x, y) α(x, y) = hue(x, y) 2 Local hue difference β(x, y) = max{hue(i, j)} − min{hue(i, j)}, (i, j) ∈ N(x, y) i,j i,j 3 Color confusability due to CVD γ(x, y) = (||(C(x, y) − C(i, j))||− (i,j)∈N(x,y) ||(Sim(C(x, y)) − Sim(C(i, j)))||)2 Jia-Bin Huang et al. (IIS, Academia Sinica) Enhancing Color Representation for the Color Vision Impaired
- 21. Color Confusability Map γ(x, y) = (||(C(x, y) − C(i, j))||− (i,j)∈N(x,y) ||(Sim(C(x, y)) − Sim(C(i, j)))||)2 1 The value of γ(x, y) in logarithmic scale (a) (b) (c) Jia-Bin Huang et al. (IIS, Academia Sinica) Enhancing Color Representation for the Color Vision Impaired
- 22. Combining Three Measurement α: hue value β: local hue difference γ: local color confusability Conventional histogram accumulating function δ(h − α) Generalized histogram accumulating function h−α S(h|α, β, γ) = γ × Rect( ) β 1, if − 0.5 ≤ x ≤ 0.5, Rect(x) ≡ 0, otherwise. Jia-Bin Huang et al. (IIS, Academia Sinica) Enhancing Color Representation for the Color Vision Impaired
- 23. Generalized Histogram Equalization By scanning over the whole image, the generalized histogram can be obtained: GH(h) = S(h|α(x, y), β(x, y), γ(x, y))dxdy Construct the hue transfer function h T (h) = hmin + (hmax − hmin) GH(h)dh hmin Jia-Bin Huang et al. (IIS, Academia Sinica) Enhancing Color Representation for the Color Vision Impaired
- 24. Controlling the Enhancement Degree Add a magnitude mapping function M(x) = xp The hue transfer function h hmin M(GH(h))dh T (h) = hmin + (hmax − hmin) hmax hmin M(GH(h))dh Jia-Bin Huang et al. (IIS, Academia Sinica) Enhancing Color Representation for the Color Vision Impaired
- 25. Outline 1 Introduction Color Vision Deficiency (CVD) Related Works 2 The Proposed Algorithm Main Idea Generalized Histogram Equalization Controlling the Enhancement Degree 3 Results and Discussion Visual Results Interactive Interface Applications 4 Conclusion Jia-Bin Huang et al. (IIS, Academia Sinica) Enhancing Color Representation for the Color Vision Impaired
- 26. Visual Results Ishihara test chart (a) (c) (e) (g) (b) (d) (f) (h) Jia-Bin Huang et al. (IIS, Academia Sinica) Enhancing Color Representation for the Color Vision Impaired
- 27. Comparison with Rasche et al’s Approach Image Simulation Our results Rasche et al’s results Jia-Bin Huang et al. (IIS, Academia Sinica) Enhancing Color Representation for the Color Vision Impaired
- 28. Comparison with Jeferson et al.’s Approach (1/2) Image Our results Jeferson et al.’s results Jia-Bin Huang et al. (IIS, Academia Sinica) Enhancing Color Representation for the Color Vision Impaired
- 29. Comparison with Jeferson et al.’s Approach (2/2) Image Our results Jeferson et al.’s results Jia-Bin Huang et al. (IIS, Academia Sinica) Enhancing Color Representation for the Color Vision Impaired
- 30. Controlling the Enhancement Degree (a) (c)p = 0.2 (e)p = 0.6 (g)p = 1.0 (b)p = 0 (d)p = 0.4 (f)p = 0.8 (h)p = 1.2 Jia-Bin Huang et al. (IIS, Academia Sinica) Enhancing Color Representation for the Color Vision Impaired
- 31. Controlling the Enhancement Degree (a) (c)p = 0.2 (e)p = 0.6 (g)p = 1.0 (b)p = 0 (d)p = 0.4 (f)p = 0.8 (h)p = 1.2 Jia-Bin Huang et al. (IIS, Academia Sinica) Enhancing Color Representation for the Color Vision Impaired
- 32. Applications Improve web accessibility for the colorblind Color adaptation in multimedia content (e.g. images, videos etc.) Assistive technologies for designers Jia-Bin Huang et al. (IIS, Academia Sinica) Enhancing Color Representation for the Color Vision Impaired
- 33. Outline 1 Introduction Color Vision Deficiency (CVD) Related Works 2 The Proposed Algorithm Main Idea Generalized Histogram Equalization Controlling the Enhancement Degree 3 Results and Discussion Visual Results Interactive Interface Applications 4 Conclusion Jia-Bin Huang et al. (IIS, Academia Sinica) Enhancing Color Representation for the Color Vision Impaired
- 34. Conclusion and Future Work Conclusion Fast re-color algorithm for CVD viewers Re-color image using local contrast information Interactive interface Future work Subjective experiments Re-coloring through optimization Jia-Bin Huang et al. (IIS, Academia Sinica) Enhancing Color Representation for the Color Vision Impaired
- 35. The End Thank you Jia-Bin Huang et al. (IIS, Academia Sinica) Enhancing Color Representation for the Color Vision Impaired