colour.difference.delta_E_CIE2000

colour.difference.delta_E_CIE2000(Lab_1, Lab_2, textiles=False)

Returns the difference \(\Delta E_{00}\) between two given CIE L*a*b* colourspace arrays using CIE 2000 recommendation.

Parameters

• Lab_1 (array_like) – CIE L*a*b* colourspace array 1.

• Lab_2 (array_like) – CIE L*a*b* colourspace array 2.

• textiles (bool, optional) – Textiles application specific parametric factors \(k_L=2,\ k_C=k_H=1\) weights are used instead of \(k_L=k_C=k_H=1\).

Returns

Colour difference \(\Delta E_{00}\).

Return type

numeric or ndarray

Notes

Domain	Scale - Reference	Scale - 1
Lab_1	L_1 : [0, 100] a_1 : [-100, 100] b_1 : [-100, 100]	L_1 : [0, 1] a_1 : [-1, 1] b_1 : [-1, 1]
Lab_2	L_2 : [0, 100] a_2 : [-100, 100] b_2 : [-100, 100]	L_2 : [0, 1] a_2 : [-1, 1] b_2 : [-1, 1]

• CIE 2000 colour differences are not symmetrical: difference between Lab_1 and Lab_2 may not be the same as difference between Lab_2 and Lab_1 thus one colour must be understood to be the reference against which a sample colour is compared.

• Parametric factors \(k_L=k_C=k_H=1\) weights under reference conditions:

  • Illumination: D65 source

  • Illuminance: 1000 lx

  • Observer: Normal colour vision

  • Background field: Uniform, neutral gray with \(L^*=50\)

  • Viewing mode: Object

  • Sample size: Greater than 4 degrees

  • Sample separation: Direct edge contact

  • Sample colour-difference magnitude: Lower than 5.0 \(\Delta E_{00}\)

  • Sample structure: Homogeneous (without texture)

References

[Lin09b], [Mel13]

Examples

>>> Lab_1 = np.array([100.00000000, 21.57210357, 272.22819350])
>>> Lab_2 = np.array([100.00000000, 426.67945353, 72.39590835])
>>> delta_E_CIE2000(Lab_1, Lab_2)  
94.0356490...
>>> Lab_2 = np.array([50.00000000, 426.67945353, 72.39590835])
>>> delta_E_CIE2000(Lab_1, Lab_2)  
100.8779470...
>>> delta_E_CIE2000(Lab_1, Lab_2, textiles=True)  
95.7920535...