1. Apparent Greyscale: A Simple and Fast Conversion to Perceptually Accurate Images and Video Kaleigh SmithPierre-Edouard Landes Joelle Thollot Karol Myszkowski

2. OUTLINE  Introduction  Related Work  Apparent Lightness  Global Apparent Lightness Mapping  Local Chromatic Contrast Adjustment  Result  Conclusion

3. Introduction  We use a two-step approach for converting complex images and video to perceptually accurate greyscale versions.  1. globally assign grey values and determine color ordering.  2. locally enhance the greyscale to reproduce the original contrast

4. Introduction  Our global mapping is image independent and incorporates the Helmholtz-Kohlrausch effect for predicting differences between isoluminant colors.  We are not too sensitive to the loss of discriminability when it occurs between spatially distant colors, but with adjacent colors it is immediately apparent.

5. Related Work  [Gooch et al.] find grey values that best match the original color differences through an objective function minimization process.  [Rasche et al.] propose a similar approach that finds the linear transform matching pairwise grey differences to corresponding color differences.  [Neumann et al.] present a technique with linear complexity that requires no user intervention.

6. Apparent Lightness  Throughout this paper, we work in the CIELAB and CIELUV color spaces, whose three axes approximate perceived lightness, saturation and hue angle.  The first component, L*, quantifies the perceptual response of a human viewer to luminance and is defined as L* = 116(Y/Y 0 ) 1/3 −16 for luminance Y and reference white luminance Y 0.

7. Apparent Lightness  While luminance is the dominant contributor to lightness perception, the chromatic component also contributes, and this contribution varies according to both hue and saturation.  The phenomenon is characterized by the Helmholtz-Kohlrausch effect, where given two isoluminant colors, the more colorful sample appears brighter.

8. Apparent Lightness  three predictors to correct L* based on the color’s chromatic component.

9. Apparent Lightness  We now decide which predictor is best suited to greyscale conversion.  In testing L* VCC, we observe that its stronger effect maps many bright colors to white, making it impossible to distinguish between very bright isoluminant colors.  L** exhibits a small range at blue hues. This range reduction makes L** becomes less discriminable.  We therefore conclude that L* VAC is the most suitable H-K predictor to use.

10. Global Apparent Lightness Mapping  The mapping process is as follows:  We first convert the color image to linear RGB by inverse gamma mapping, then transform to CIELUV color space.  Its apparent chromatic object lightness channel L* VAC is calculated according to (2). We map L* VAC to greyscale Y values using reference white chromatic values for u* and v*.  Finally, we apply gamma mapping to move from linear Y space back to a gamma-corrected greyscale image G

11. Global Apparent Lightness Mapping  Due to the compression of a 3D gamut to 1D, L* VAC may map two different colors to a similar lightness, which then are quantized to the same grey value.  This occurs only when colors differ uniquely by hue, which is very uncommon in natural images and well- designed graphics.  Our global mapping partially solves the problem of grey value assignment and appropriately orders colors that normal luminance mapping can not discriminate.

12. Local Chromatic Contrast Adjustment  Because of dimension reduction and unaccounted for hue differences, chromatic contrast may be reduced.  Humans are most sensitive to these losses at local contrasts, regions where there is a visible discontinuity.  To counter the reduction, we increase local contrast in the greyscale image G to better represent the local contrast of original I.

13. Local Chromatic Contrast Adjustment  We perform contrast adjustments using the Laplacian pyramid that decomposes an image into n bandpass images h i and a single lowpass image l  At each scale in the Laplacian pyramid, we adaptively increase local contrast h i (G L* ) by a perceptually-based amount λ i, which measures the amount of contrast needed to match color contrast h i (I).

14. Local Chromatic Contrast Adjustment

15. Result

19. Conclusion  We have presented a new approach to color to grey conversion. Our approach offers a more perceptually accurate appearance.  The main limitation of our approach is the locality of the second step (local contrast adjustment). It can not restore chromatic contrast between non-adjacent regions.  This step also risks introducing temporal inconsistencies.