Chromametry

A metrics framework for evaluating web-accessible sequential color palettes.

Figure 1. Web-accessible Palette Ranking

Metric Definitions

1. Contrast Efficiency: Measures how efficiently contrast space is used to achieve WCAG 4.5:1 contrast.
2. Lightness Linearity: Evaluates the linearity of lightness (with Helmholtz–Kohlrausch correction).
3. Chroma Smoothness: Checks for artifacts and kinks in the saturation curve using Monotone Cubic Splines.
4. Hue Stability: Quantifies hue shift/drift across the lightness ramp.
5. Spacing Uniformity: Measures the consistency of color spacing (DeltaE 2000).

   Read full Methodology

All lightness, chroma, hue, and DeltaE2000 computations are performed in the CIELAB color space.

Benchmark Rankings

Comparison of popular design systems based on Chromametry metrics.

Table 1. Benchmark ranking of design systems evaluated using Chromametry metrics.

Note: Design systems like Bootstrap,Google Material 3, Apple Human Interface or Fluent UI are excluded as they define discrete semantic tokens rather than algorithmic sequential ramps.

Overall Score is computed as the weighted mean of the five normalized metrics, using equal weights by default.

Example: A Typical Report

Figure 2. IBM Carbon Color Palette.

Figure 3. IBM Carbon Palette Metrics.

Figure 4. IBM Carbon Palette Charts.

Benchmark result page

• Online Report : Benchmark page
• Local /benchmarks/monochromatic/output/index.html (double click)

Analyze Palettes

Installation

NPM Package:

npm install chromametry

CDN (Browser) ESM:

<script type="module">
  import { analyzeMonochromaticPalette } from 'https://esm.sh/chromametry';
</script>
  const result = Chromametry.analyzeMonochromaticPalette({ ... });
</script>

CDN (Browser) Global:

<script src="https://unpkg.com/chromametry/dist/index.global.js"></script>
<script>
  const result = Chromametry.analyzeMonochromaticPalette({ ... });
</script>

Usage

import { analyzeMonochromaticPalette } from 'chromametry';

// 1. Define your colors (must include white/black anchors for accurate scoring)
const colors = {
  yellow: ["#ffffff","#fcf4d6","#fddc69","#f1c21b","#d2a106","#b28600","#8e6a00","#684e00","#483700","#302400","#1c1500","#000000"],
  orange: ["#ffffff","#fff2e8","#ffd9be","#ffb784","#ff832b","#eb6200","#ba4e00","#8a3800","#5e2900","#3e1a00","#231000","#000000"]
};

// 2. Define step names corresponding to the array length
const stepNames = [0, 100, 200, 300, 400, 500, 600, 700, 800, 900, 950,1000];

// 3. Run analysis
const result = analyzeMonochromaticPalette({
    name: "My Custom Brand",
    stepNames: stepNames,
    colors: colors
});

console.log(result.metrics); 
console.log(`System Score: ${result.score}`);

Reproducing Benchmarks

To run the benchmark generator locally:

git clone https://github.com/chromametry/chromametry.git
cd chromametry
npm install
npm run generate

Adding Custom Palettes to Benchmark

Create a new .ts file in benchmarks/monochromatic/input/ (e.g., my-palette.ts).

import { MonochromaticPaletteData } from "../../../src/index.js";
import { red, volcano, gold } from '@ant-design/colors';

// Define colors (imported or inline object)
let colors: Record<string, string[]> = { red, volcano, gold };

// Ensure white/black anchors exist if your ramp misses them
for (let name in colors) {
    if (colors[name][0] !== "#ffffff") colors[name].unshift("#ffffff");
    if (colors[name][colors[name].length - 1] !== "#000000") colors[name].push("#000000");
}

const stepNames = Object.keys(Object.values(colors)[0]);

const palette: MonochromaticPaletteData = {
    name: "Ant Design",
    stepNames, // string[]
    colors     // Record<string, string[]> 
};

export default palette;

Then regenerate the report:

npm run generate

Input Requirements

• Equal Steps: All color ramps must have the same number of steps.

• Format: Colors must be Hex strings.

• Monotonicity: Lightness must strictly increase or decrease (sorted).

• Anchors: Start/End colors should ideally be Black and White.