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

Apparent Greyscale: A Simple and Fast Conversion

to Perceptually Accurate Images and Video

Kaleigh Smith Pierre-Edouard Landes

Joelle Thollot Karol Myszkowski

OUTLINE

Introduction Related Work Apparent Lightness Global Apparent Lightness Mapping Local Chromatic Contrast Adjustment Result Conclusion

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 col

or ordering. 2. locally enhance the greyscale to reproduce th

e original contrast

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.

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.

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/Y0)1/3 −16 for luminance Y and reference white luminance Y0.

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.

Apparent Lightness

three predictors to correct L* based on the color’s chromatic component.

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.

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

Global Apparent Lightness Mapping Due to the compression of a 3D gamut to 1D, L*VAC m

ay 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.

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.

Local Chromatic Contrast Adjustment

We perform contrast adjustments using the Laplacian pyramid that decomposes an image into n bandpass images hi and a single lowpass image l

At each scale in the Laplacian pyramid, we adaptively increase local contrast hi(GL*) by a perceptually-based amount λi, which measures the amount of contrast needed to match color contrast hi(I).

Local Chromatic Contrast Adjustment

Result

Result

Result

Result

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.