enhancing color representation for the color vision impaired (cvavi 2008)

Enhancing Color Representation for the ColorVision Impaired

Jia-Bin Huang1, Sih-Ying Wu2, and Chu-Song Chen1

[email protected]

1 Institute of Information ScienceAcademia Sinica

2Department of Electronics EngineeringNational Chiao Tung University

October 18, 2008

Jia-Bin Huang et al. (IIS, Academia Sinica) Enhancing Color Representation for the Color Vision Impaired

Outline

1 IntroductionColor Vision Deficiency (CVD)Related Works

2 The Proposed AlgorithmMain IdeaGeneralized Histogram EqualizationControlling the Enhancement Degree

3 Results and DiscussionVisual ResultsInteractive InterfaceApplications

4 Conclusion


Color Vision

Normal visionThree different fundamental photoreceptor cells (cone cells)Peak responses lie in long (L), middle (M), and short (S)wavelength regions


Classification of CVD

Anomalous trichromacyOne of the three cone cells is abnormal

DichromacyOne of the three cone cells is absent

MonochromacyAll three cone cells are absent

Table: Major genetic color deficiencies.

Type Name Cause of defect

Anomalous trichromacyProtanomaly L-cone defectDeuteranomaly M-cone defectTritanomaly S-cone defect

DichromacyProtanopia L-cone absentDeuteranopia M-cone absentTritanopia S-cone absent

Monochromacy Rod Monochromacy no functioning cones


How People with CVD perceive Colors?

Anomalous trichromacy

Protanomaly Deuteranomaly TritanomalyDichromacy

Protanopia Deuteranopia TritanopiaJia-Bin Huang et al. (IIS, Academia Sinica) Enhancing Color Representation for the Color Vision Impaired

Related Works

1 Guideline or assistant tool for designers to avoidambiguous color combinations

Provide guideline (Chisholm et al. 01)Use a restricted CVD palette (Rigden et al. 99, Vienot99 etal. 99)Verify color schemes (Walraven et al.97, Jenny07 et al. 07)

2 (Semi-)automatically reproduce colors that are suitable forCVD viewers


Typical Re-coloring Approaches

Common procedures

Select key colors using color quantization

Find the optimal mapping of key colors using optimizationprocedure

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


Outline




4 Conclusion


Main Idea

Main Idea

Maintain luminance and saturation consistency.=>Work in the HSV color space, and leave value andsaturation component unchanged

Preserve the natural order of hue.=>Apply a hue transfer function to hue channel(non-decreasing)


Contrast Enhancement using Histogram Equalization(HE)

One of the most well-known technique for imageenhancement

Assign larger dynamic range for intensity values havinglarger number of occurrences

T(g) = gmin + (gmax − gmin)

∫g

gmin

hist(g) dg

T(g): Intensity mapping function

hist(g): Normalized histogram (probability distribution ofthe grey levels in the image)

gmax, gmin: Maximum and minimum intensity values


Histogram Generalization

Original Histogram

Histogram generationMasking-and-accumulating using 1 × 1 block

Generalized Histogram

Generalized histogramExtend 1 × 1 block to N × N to encode spatial informationReplace sampling by measurement(feature extraction)


Three Local Measurement

1 Hue value at point (x, y)

α(x, y) = hue(x, y)

2 Local hue difference

β(x, y) = maxi,j

{hue(i, j)} − mini,j

{hue(i, j)}, (i, j) ∈ N(x, y)

3 Color confusability due to CVD

γ(x, y) =∑

(i,j)∈N(x,y)

(||(C(x, y) − C(i, j))||−

||(Sim(C(x, y)) − Sim(C(i, j)))||)2


Color Confusability Map

γ(x, y) =∑

(i,j)∈N(x,y)

(||(C(x, y) − C(i, j))||−

||(Sim(C(x, y)) − Sim(C(i, j)))||)2

1 The value of γ(x, y) in logarithmic scale

(a) (b) (c)


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

S(h|α,β, γ) = γ × Rect(h − α

β)

Rect(x) ≡

{

1, if − 0.5 ≤ x ≤ 0.5,0, otherwise.


Generalized Histogram Equalization

By scanning over the whole image, the generalizedhistogram can be obtained:

GH(h) =

∫ ∫

S(h|α(x, y), β(x, y), γ(x, y))dxdy

Construct the hue transfer function

T(h) = hmin + (hmax − hmin)

∫h

hmin

GH(h)dh


Controlling the Enhancement Degree

Add a magnitude mapping function

M(x) = xp

The hue transfer function

T(h) = hmin + (hmax − hmin)

∫h

hminM(GH(h))dh

∫hmax

hminM(GH(h))dh


Outline




4 Conclusion


Visual Results

Ishihara test chart

(a) (c) (e) (g)

(b) (d) (f) (h)


Comparison with Rasche et al’s Approach

Image Simulation Our results Rasche et al’s results


Comparison with Jeferson et al.’s Approach (1/2)

Image Our results Jeferson et al.’s results


Comparison with Jeferson et al.’s Approach (2/2)

Image Our results Jeferson et al.’s results


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


Applications

Improve web accessibility for the colorblind

Color adaptation in multimedia content (e.g. images,videos etc.)

Assistive technologies for designers


Outline




4 Conclusion


Conclusion and Future Work

ConclusionFast re-color algorithm for CVD viewersRe-color image using local contrast informationInteractive interface

Future workSubjective experimentsRe-coloring through optimization


The End

Thank you


enhancing color representation for the color vision impaired (cvavi 2008)

Documents

suitable forcvd viewersjia

verify color schemes

color vision impairedcomparison

enhancing color representation

color vision impairedtypical

color vision impairedcontrolling

automatically reproduce colors

enhancement degree3 results