Color difference

 ( Color Space ) 

In color science, color difference or color distance is the separation between two . This metric space allows quantified examination of a notion that formerly could only be described with adjectives. Quantification of these properties is of great importance to those whose work is color-critical. Common definitions make use of the Euclidean distance in a device-independent color space.

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Euclidean

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sRGB As most definitions of color difference are distances within a color space, the standard means of determining distances is the Euclidean distance. If one presently has an RGB (red, green, blue) tuple and wishes to find the color difference, computationally one of the easiest is to consider R, G, B linear dimensions defining the color space.

A very simple example can be given between the two colors with RGB values (0, 64, 0) () and (255, 64, 0) (): their distance is 255. Going from there to (255, 64, 128) () is a distance of 128.

When we wish to calculate distance from the first point to the third point (i.e. changing more than one of the color values), we can do this:

$$\backslash text\{distance\}\; =\; \backslash sqrt\{(R\_2\; -\; R\_1)^2\; +\; (G\_2\; -\; G\_1)^2\; +\; (B\_2\; -\; B\_1)^2\}.$$

When the result should be computationally simple as well, it is often acceptable to remove the square root and simply use

$$\backslash text\{distance\}^2\; =\; (R\_2\; -\; R\_1)^2\; +\; (G\_2\; -\; G\_1)^2\; +\; (B\_2\; -\; B\_1)^2.$$

This will work in cases when a single color is to be compared to a single color and the need is to simply know whether a distance is greater. If these squared color distances are summed, such a metric effectively becomes the variance of the color distances.

There have been many attempts to weigh RGB values, however these are demonstrably worse at color determinations and are properly the contributions to the brightness of these colors, rather than to the degree to which human vision has less tolerance for these colors. The closer approximations would be more properly (for non-linear sRGB, using a color range of 0–255): $$\backslash begin\{cases\}$$

\sqrt{2 \Delta R^2 + 4 \Delta G^2 + 3 \Delta B^2}, & \bar R < 128, \\
\sqrt{3 \Delta R^2 + 4 \Delta G^2 + 2 \Delta B^2} & \text{otherwise},
     

\end{cases}

where: $$\backslash begin\{aligned\}$$

\Delta R &= R_1 - R_2, \\
\Delta G &= G_1 - G_2, \\
\Delta B &= B_1 - B_2, \\
\bar R &= \frac12 (R_1 + R_2).
     

\end{aligned}

One of the better low-cost approximations, sometimes called "redmean", combines the two cases smoothly:

$$\backslash begin\{aligned\}$$

\bar r &= \frac12 (R_1 + R_2), \\
\Delta C &= \sqrt{\left(2 + \frac{\bar r}{256}\right) \Delta R^2 + 4 \Delta G^2 + \left(2 + \frac{255 - \bar r}{256}\right) \Delta B^2}.
     

\end{aligned}()

There are a number of color distance formulae that attempt to use color spaces like HSV or HSL with the hue represented as a circle, placing the various colors within a three-dimensional space of either a cylinder or cone, but most of these are just modifications of RGB; without accounting for differences in human color perception, they will tend to be on par with a simple Euclidean metric.

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Uniform color spaces CIELAB and CIELUV are relatively perceptually-uniform color spaces and they have been used as spaces for Euclidean measures of color difference. The CIELAB version is known as CIE76. However, the non-uniformity of these spaces were later discovered, leading to the creation of more complex formulae.

A uniform color space is supposed to make a simple measure of color difference, usually Euclidean, "just work". Color spaces that improve on this issue include CAM02-UCS, CAM16-UCS, and J_za_zb_z.

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Rec. ITU-R BT.2124 or ΔE_ITP In 2019 a new standard for WCG and HDR was introduced, since CIEDE2000 was not adequate for it: CIEDE2000 is not reliable below 1 cd/m² and has not been verified above 100 cd/m²; in addition, even in BT.709 blue primary CIEDE2000 is underpredicting the error. Δ E_ITP is scaled so that a value of 1 indicates the potential of a just noticeable color difference. The Δ E_ITP color difference metric is derived from display referenced ICtCp, but XYZ is also available in the standard. The formula is a simply scaled Euclidean distance:

$$\backslash Delta\; E\_\backslash text\{ITP\}\; =\; 720\; \backslash sqrt\{(I\_1\; -\; I\_2)^2\; +\; (T\_1\; -\; T\_2)^2\; +\; (P\_1\; -\; P\_2)^2\},$$

where the components of this "ITP" is given by

I = I,

T = 0.5 C _T,

P = C _P.

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Other geometric constructions The Euclidean measure is known to work poorly on large color distances (i.e. more than 10 units in most systems). On CIELAB, a hybrid approach where a taxicab distance is used between the lightness and the chroma plane, $\backslash Delta\; E\_\{\backslash text\{HyAB\}\}\; =\; \backslash sqrt\{\; (a\_2-a\_1)^2+(b\_2-b\_1)^2\; \}\; +\; \backslash left|\; L\_2-L\_1\; \backslash right|$, is shown to work better than Euclidean and CIEDE2000.

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CIELAB ΔE* The International Commission on Illumination (CIE) calls their distance metric (also inaccurately called , , or "Delta E") where delta is a Greek letter often used to denote difference, and E stands for Empfindung; German for "sensation". Use of this term can be traced back to Hermann von Helmholtz and Ewald Hering.

(1998). 9783110154313, Walter de Gruyter. . ISBN 9783110154313

A. Valberg (2026). 9780470849026, Wiley. . ISBN 9780470849026

Perceptual non-uniformities in the underlying CIELAB color space have led to the CIE refining their definition over the years, leading to the superior (as recommended by the CIE) 1994 and 2000 formulas.

These non-uniformities are important because the human eye is more sensitive to certain colors than others. CIELAB metric is used to define color tolerance of CMYK solids. A good metric should take this into account in order for the notion of a "just noticeable difference" (JND) to have meaning. Otherwise, a certain may be insignificant between two colors in one part of the color space while being significant in some other part. Evaluation of the CIE Color Difference Formulas

All formulae are originally designed to have the difference of 1.0 stand for a JND. This convention is generally followed by other perceptual distance functions such as the aforementioned . However, further experimentation may invalidate this design assumption, the revision of CIE76 JND to 2.3 being an example.

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CIE76 The CIE 1976 color difference formula is the first formula that related a measured color difference to a known set of CIELAB coordinates. This formula has been succeeded by the 1994 and 2000 formulas because the CIELAB space turned out to be not as perceptually uniform as intended, especially in the saturated regions. This means that this formula rates these colors too highly as opposed to other colors.

Given two colors in CIELAB color space, $(\{L^*\_1\},\{a^*\_1\},\{b^*\_1\})$ and $(\{L^*\_2\},\{a^*\_2\},\{b^*\_2\})$, the CIE76 color difference formula is defined as:

$$\backslash Delta\; E\_\{ab\}^*\; =\; \backslash sqrt\{\; (L^*\_2-L^*\_1)^2+(a^*\_2-a^*\_1)^2\; +\; (b^*\_2-b^*\_1)^2\; \}.$$

$\backslash Delta\; E\_\{ab\}^*\; \backslash approx\; 2.3$ corresponds to a JND (just noticeable difference).

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CMC l:c (1984) In 1984, the Colour Measurement Committee of the Society of Dyers and Colourists defined a difference measure based on the CIE L*C*h color model, an alternative representation of L*a*b* coordinates. Named after the developing committee, their metric is called CMC l:c. The quasimetric (i.e. it violates symmetry: parameter T is based on the hue of the reference $h\_1$ alone) has two parameters: lightness (l) and chroma (c), allowing the users to weight the difference based on the ratio of l:c that is deemed appropriate for the application. Commonly used values are 2:1Meaning that the lightness contributes half as much to the difference (or, identically, is allowed twice the tolerance) as the chroma for acceptability and 1:1 for the threshold of imperceptibility.

The distance of a color $(L^*\_2,C^*\_2,h\_2)$ to a reference $(L^*\_1,C^*\_1,h\_1)$ is:

$$\backslash Delta\; E^*\_\{CMC\}\; =\; \backslash sqrt\{\; \backslash left(\; \backslash frac\{L^*\_2-L^*\_1\}\{l\; \backslash times\; S\_L\}\; \backslash right)^2\; +\; \backslash left(\; \backslash frac\{C^*\_2-C^*\_1\}\{c\; \backslash times\; S\_C\}\; \backslash right)^2\; +\; \backslash left(\; \backslash frac\{\backslash Delta\; H^*\_\{ab\}\}\{S\_H\}\; \backslash right)^2\; \}$$

CMC l:c is designed to be used with D65 and the CIE Supplementary Observer.

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CIE94 The CIE 1976 color difference definition was extended to address perceptual non-uniformities, while retaining the CIELAB color space, by the introduction of application-specific parametric weighting factors k_L, k_C and k_H, and functions S_L, S_C, and S_H derived from an automotive paint test's tolerance data.

As with the CMC I:c, Δ E (1994) is defined in the L*C*h* color space and likewise violates symmetry, therefore defining a quasimetric. Given a reference color both depend on $C^*\_1$ only.}} $(L^*\_1,\; a^*\_1,\; b^*\_1)$ and another color $(L^*\_2,\; a^*\_2,\; b^*\_2)$, the difference is

$$\backslash Delta\; E\_\{94\}^*\; =\; \backslash sqrt\{\backslash left(\backslash frac\{\backslash Delta\; L^*\}\{k\_L\; S\_L\}\backslash right)^2\; +\; \backslash left(\backslash frac\{\backslash Delta\; C^*\}\{k\_C\; S\_C\}\backslash right)^2\; +\; \backslash left(\backslash frac\{\backslash Delta\; H^*\}\{k\_H\; S\_H\}\backslash right)^2\},$$

where

$$\backslash begin\{aligned\}$$

\Delta L^* &= L^*_1 - L^*_2, \\
C^*_1 &= \sqrt \sin \left[ 60^\circ \cdot \exp \left( -\left[ \frac{\bar{h}'-275^\circ}{25^\circ} \right]^2 \right) \right]
     

CIEDE 2000 is not mathematically continuous. The discontinuity stems from the discontinuity of the mean hue $\backslash bar\{h\}^\backslash prime$ and the hue difference $\backslash Delta\; h\text{'}$. The maximum discontinuity happens when the hues of two sample colors are about 180° apart, and is usually small relative to ΔE (less than 4%). There is also a negligible amount of discontinuity from hue rollover.

Sharma, Wu, and Dalal have provided additional notes on the mathematics and implementation of the formulae.

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Tolerance Tolerancing concerns the question "What is a set of colors that are imperceptibly/acceptably close to a given reference?" If the distance measure is perceptually uniform, then the answer is simply "the set of points whose distance to the reference is less than the just-noticeable-difference (JND) threshold". This requires a perceptually uniform metric in order for the threshold to be constant throughout the gamut (range of colors). Otherwise, the threshold will be a function of the reference color—cumbersome as a practical guide.

In the CIE 1931 color space, for example, the tolerance contours are defined by the MacAdam ellipse, which holds L* (lightness) fixed. As can be observed on the adjacent diagram, the denoting the tolerance contours vary in size. It is partly this non-uniformity that led to the creation of CIELUV and CIELAB.

More generally, if the lightness is allowed to vary, then we find the tolerance set to be . Increasing the weighting factor in the aforementioned distance expressions has the effect of increasing the size of the ellipsoid along the respective axis.

The definition of "acceptably close" also depends on the industrial requirements and practicality. In the automotive industry the is rather stringent, often less than 0.5 under D65/10. In printing, the typical limit is 2.0 under D50, though some processes require up to 5.0.

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See also

• CIELAB
• Color coding in data visualization

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Footnotes

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Notes

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Further reading

• (1997). 9780901956705, Society of Dyers and Colourists. ISBN 9780901956705

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External links

• Bruce Lindbloom's color difference calculator. Uses all CIELAB metrics defined herein.
• The CIEDE2000 Color-Difference Formula, by Gaurav Sharma. Implementations in MATLAB and Excel.
• Explore the Spectrum with Colors in Between, by Bettie M. Cobb.
• Excel add-in for color difference calculations and color space conversions, by Edgardo García.
• Michel Leonard's CIE ΔE 2000 implementations consistent in 40+ programming languages.

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