# colour.difference.delta_E_CIE1994#

colour.difference.delta_E_CIE1994(Lab_1: ArrayLike, Lab_2: ArrayLike, textiles: bool = False) NDArrayFloat[source]#

Return the difference $$\Delta E_{94}$$ between two given CIE L*a*b* colourspace arrays using CIE 1994 recommendation.

Parameters:
• Lab_1 (ArrayLike) – CIE L*a*b* colourspace array 1.

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

• textiles (bool) – Textiles application specific parametric factors, $$k_L=2,\ k_C=k_H=1,\ k_1=0.048,\ k_2=0.014$$ weights are used instead of $$k_L=k_C=k_H=1,\ k_1=0.045,\ k_2=0.015$$.

Returns:

Colour difference $$\Delta E_{94}$$.

Return type:

numpy.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 1994 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.

References

[Lin11]

Examples

>>> Lab_1 = np.array([100.00000000, 21.57210357, 272.22819350])
>>> Lab_2 = np.array([100.00000000, 426.67945353, 72.39590835])
>>> delta_E_CIE1994(Lab_1, Lab_2)
83.7792255...
>>> delta_E_CIE1994(Lab_1, Lab_2, textiles=True)
88.3355530...