color difference （deltaE）

cThe color instance contains a deltaE method. Can be used to query color difference, it supports CMC, CIE2000, CIE1994, CIE1976 color difference formula query

color.deltaE(sampleColor: Color, mode?: DeltaEMode, setting?: DeltaESetting) => number

Note: The current color instance object color is standard color, the first parameter sampleColor is sample color

Parameter Description:

• @param sampleColor sample color
• @param mode Color difference mode, which formula to use to calculate color difference, the default value is CMC, there are the following options: CMC | CIE2000 | CIE1994 | CIE1976
• @param setting Different color difference formulas have different coefficients that can be set
  • CMC: { l: number, c: number } The default value is { l: 1, c: 1 }
  • CIE2000: { kL: number; kC: number; kH: number } The default value is { kL: 1; kC: 1; kH: 1 }
  • CIE1994:{ kL: number; kC: number; kH: number; cate: 'graphic' | 'textiles' } The default value is { kL: 1; kC: 1; kH: 1, cate: 'graphic' }, When cate is textiles, the value of kL will become 2, regardless of the setting

CMC(l:c)

Example：

const color1 = colorsea.lab(80, 30, 120) // Standard color (reference color)
const color2 = colorsea.lab(79, 28, 100) // Sample color

// Use the CMC(1:1) formula
color1.deltaE(color2, 'CMC') // 5.754...
// The second parameter defaults to 'CMC', so the second parameter can be omitted
color1.deltaE(color2)

// CMC(2:1) formula, just set 'l' to 2 ('c' defaults to 1)
color1.deltaE(color2, 'CMC', { l: 2 }) // 5.719...

TIP

In the CMC (l:c) color difference formula, l represents the weighted value of lightness, which adjusts the relative width of lightness; c represents the weighted value of chroma, which adjusts the relative width of chroma. Depending on the industry, you can adjust the influence of lightness and saturation on the total color difference by adjusting the value of l or c.

• When evaluating the perception of color difference, it is recommended to use l:c = 1:1, such as paint, plastic industry.
• When evaluating the acceptability of color difference, it is recommended to use l:c = 2: 1, and it is recommended to use l:c = 2: 1 to control product quality in textile printing and dyeing industries

CIE2000

Example：

const color1 = colorsea.lab(80, 30, 120) // Standard color (reference color)
const color2 = colorsea.lab(79, 28, 100) // Sample color

// Using the CIE2000 formula
color1.deltaE(color2, 'CIE2000') // 3.6815...

// Efficacy coefficient, kL, kC, kH default value is 1
color1.deltaE(color2, 'CIE2000', { kL：1, kC: 1: kH: 1})

CIE1994

Example：

const color1 = colorsea.lab(80, 30, 120) // Standard color (reference color)
const color2 = colorsea.lab(79, 28, 100) // Sample color

// Using the CIE1994 formula
color1.deltaE(color2, 'CIE1994') // 3.3725...

// Effect coefficient, kL, kC, kH default value is 1
color1.deltaE(color2, 'CIE1994', { kL：1, kC: 1: kH: 1})

// Note: The default value of cate is 'graphic', that is, graphic arts
// If used for fabric evaluation, set cate to 'textiles'.
color1.deltaE(color2, 'CIE1994', { cate: 'textiles'})

TIP

When { cate: 'textiles' }, the coefficient kL will ignore the custom setting and automatically become 2.

CIE1976

Example：

const color1 = colorsea.lab(80, 30, 120) // Standard color (reference color)
const color2 = colorsea.lab(79, 28, 100) // Sample color

// Using the CIE1976 formula
color1.deltaE(color2, 'CIE1976') // 20.1246...