Categorical Minimum Delta-E Pairwise

Audit all categorical same-white CIELAB palette pairs for minimum CIE76 separation, exposing closest first-ordered pair; not a scalar CIE76 alias, categorical readability guarantee or color-deficiency simulation.

Description

Categorical color separation can be audited across every palette pair.

Categorical color separation can be audited across every palette pair. CIE 76 uses Euclidean distance in one Lab reference frame.^[1]

Enter 2–128 same-white Lab rows with L 0–100,a/b±200. Search pairs in original i<j order; exact ties retain the first pair. Return minimum and zero-based closest indexes.

When to use Categorical Minimum Delta-E Pairwise

Use Categorical Minimum Delta-E Pairwise when you need to evaluate the supplied records under the calculation and evidence rules below. No categorical readability guarantee or color-deficiency simulation is supplied.

Prepare colors in the stated color space and scale. Use encoded RGB fractions, linear RGB, Lab, xy, byte values or ink percentages exactly as requested; supply caller tolerances and reference conditions.

How to use Categorical Minimum Delta-E Pairwise and understand its results

Prepare colors in the stated color space and scale. Use encoded RGB fractions, linear RGB, Lab, xy, byte values or ink percentages exactly as requested; supply caller tolerances and reference conditions.
Enter categoricalLabRows using the field table and calculation rules, then run the tool.
Read minimumPairCie76Difference, closestPairIndices. The explanation below defines the units, signs, ordering and any flags or placeholder values.
If the tool reports an input issue, correct the values and run it again. Apply the stated assumptions when interpreting a successfully returned result.

Input field	Meaning	Value type; see rules below	Requirement
categoricalLabRows	Categorical lab rows	list of list of number	Required

Enter 2–128 same-white Lab rows with L 0–100,a/b±200. Search pairs in original i<j order; exact ties retain the first pair. Return minimum and zero-based closest indexes.

distance=hypot(ΔL,Δa,Δb); minimum=min(all pairs)

Units, indexing, boundary conditions and exceptional cases are defined in the calculation rules above.

Output field	Meaning	Value type
minimumPairCie76Difference	Minimum pair cie76 difference	number
closestPairIndices	Closest pair indices	list of number

json

{
  "input": {
    "categoricalLabRows": [
      [
        50,
        0,
        0
      ],
      [
        60,
        0,
        0
      ],
      [
        80,
        0,
        0
      ]
    ]
  },
  "output": {
    "minimumPairCie76Difference": 10,
    "closestPairIndices": [
      0,
      1
    ]
  }
}

Limitations and assumptions

No categorical readability guarantee or color-deficiency simulation is supplied.

Numeric inputs, where present, must be finite. Follow the calculation-specific rules for overflow, underflow and exceptional cases. Matrices are limited to 128 rows and 128 entries per row. Numeric lists are limited to 10,000 entries; string lists to 128 strings of at most 128 characters each. The rules above may impose smaller limits. Missing required fields, extra fields, wrong value types and violations of the calculation rules are input errors.

Alternative or Complementary methods

Use Brand Color Perceptual Distance Rank: Brand swatch ranking can compare caller references within one Lab frame. Stable ties preserve original provenance. For a complementary task, use Color-Blind Safe Palette Band Check: Palette distance under three dichromacy simulations provides a model-based separation screen. It cannot guarantee accessibility for every viewer.

References

Categorical Minimum Delta-E Pairwise — tool.io

Similar or alternative tools

Brand Color Perceptual Distance Rank
Brand swatch ranking can compare caller references within one Lab frame. Stable ties preserve original provenance.
Color-Blind Safe Palette Band Check
Palette distance under three dichromacy simulations provides a model-based separation screen. It cannot guarantee accessibility for every viewer.