Deuteranopia Simulation Matrix

Apply Machado et al. 2009 full-severity deutan matrix in linear sRGB then encode, reporting clipping. Model visualization only, not diagnosis, an exact individual view or accessibility certification.

Description

A deuteranopia visualization applies a full-severity model to linear sRGB.

A deuteranopia visualization applies a full-severity model to linear sRGB. Returned clipping evidence shows lost channel excursions.^{[1, 2]}

Enter three normalized encoded sRGB channels 0–1. Decode, apply Machado 2009 deutan matrix, clamp linear channels and encode. Return RGB and strict excursion indexes.

When to use Deuteranopia Simulation Matrix

Use Deuteranopia Simulation Matrix when you need to evaluate the supplied records under the calculation and evidence rules below. No clinical diagnosis, exact individual view or accessibility certification 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 Deuteranopia Simulation Matrix 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 baseRgb using the field table and calculation rules, then run the tool.
Read simulatedRgb, clippedLinearChannelIndices. 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
baseRgb	Base rgb	list of number	Required

Enter three normalized encoded sRGB channels 0–1. Decode, apply Machado 2009 deutan matrix, clamp linear channels and encode. Return RGB and strict excursion indexes.

encodedRGB → linearRGB → deutan matrix → clamp → encode

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

Output field	Meaning	Value type
simulatedRgb	Simulated rgb	list of number
clippedLinearChannelIndices	Clipped linear channel indices	list of number

json

{
  "input": {
    "baseRgb": [
      1,
      0,
      0
    ]
  },
  "output": {
    "simulatedRgb": [
      0.6400595551963536,
      0.5658069412154982,
      0
    ],
    "clippedLinearChannelIndices": [
      2
    ]
  }
}

Limitations and assumptions

No clinical diagnosis, exact individual view or accessibility certification is supplied.

Numeric inputs, where present, must be finite. Follow the calculation-specific rules for overflow, underflow and exceptional cases. 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 Protanopia Simulation Matrix: A protanopia visualization applies a published full-severity model in linear sRGB. Clipping remains visible in the returned evidence. For a complementary task, use Tritanopia Simulation Matrix: A tritanopia visualization can transform linear-light RGB with a published full-severity matrix. It represents a model rather than an individual view.

Further background: A physiologically-based model for simulation of color vision deficiency (2009).

References

Deuteranopia Simulation Matrix — tool.io
A physiologically-based model for simulation of color vision deficiency (2009) — Machado, Oliveira and Fernandes

Similar or alternative tools

Protanopia Simulation Matrix
A protanopia visualization applies a published full-severity model in linear sRGB. Clipping remains visible in the returned evidence.
Tritanopia Simulation Matrix
A tritanopia visualization can transform linear-light RGB with a published full-severity matrix. It represents a model rather than an individual view.