helmlab 0.9.2

A data-driven analytical color space trained on 64,000 human color-difference observations

Helmlab

A data-driven analytical color space for UI design systems.

Helmlab is a family of purpose-built color spaces: MetricSpace (72-parameter enriched pipeline for perceptual distance) and GenSpace (generation-optimized pipeline for gradients and palettes). MetricSpace achieves STRESS 23.30 on COMBVD (3,813 color pairs) — a 20.1% improvement over CIEDE2000. GenSpace + arc-length reparameterization produces perfectly uniform gradients (CV ≈ 0% on any color pair).

Website | Documentation | Demo | Paper

Key Features

• State-of-the-art color difference prediction — STRESS 23.30 vs CIEDE2000's 29.18 (MetricSpace)
• Perfectly uniform gradients — Built-in CIEDE2000 arc-length reparameterization in gradient(), CV ≈ 0% on any pair
• Achromatic guarantee — Grays map to C < 10⁻⁶ via neutral correction (no color artifacts in gradients)
• Free hue improvement — Rigid rotation reduces hue error (RMS 16.1°) at zero cost to the distance metric
• Embedded Helmholtz-Kohlrausch — Lightness is chroma-dependent, learned from data
• UI tooling — Gamut mapping, WCAG contrast enforcement, palette generation, dark/light mode adaptation
• Token export — CSS (oklch()), Android XML, iOS Swift (Display P3), Tailwind, JSON

Installation

npm (TypeScript / JavaScript)

npm install helmlab

import { Helmlab } from 'helmlab';

const hl = new Helmlab();

const lab = hl.fromHex('#3B82F6');                    // Hex → Helmlab Lab
const hex = hl.toHex([0.5, -0.1, 0.2]);              // Lab → hex (gamut mapped)
hl.contrastRatio('#ffffff', '#3B82F6');                // → 3.68
hl.ensureContrast('#3B82F6', '#ffffff', 4.5);         // Adjust to meet 4.5:1
hl.deltaE('#ff0000', '#00ff00');                      // Perceptual distance
hl.gradient('#ff0000', '#0000ff', 8);                 // Perfectly uniform gradient
hl.semanticScale('#3B82F6');                          // Tailwind-style 50–950 scale

~12KB gzipped, zero dependencies, ESM + CJS with full TypeScript types. See the npm package README for the full API.

PostCSS

Use Helmlab color spaces directly in CSS — transformed to rgb() at build time:

npm install postcss-helmlab

/* Input */
.card { color: helmlab(0.78 0.52 -0.20); }
.bg   { background: linear-gradient(in helmgen, #e63946, #457b9d); }

/* Output */
.card { color: rgb(255, 76, 119); }
.bg   { background: linear-gradient(#e63946 0.0%, ..., #457b9d 100.0%); }

Supports helmlab(), helmlch(), helmgen(), helmgenlch(), alpha, linear-gradient(in helmgen, ...), and color-mix(in helmlab, ...). See postcss-helmlab.

Python (pip)

pip install helmlab

Quick Start (Python)

from helmlab import Helmlab

hl = Helmlab()

# sRGB to Helmlab Lab
lab = hl.from_srgb([0.2, 0.5, 0.8])
print(f"L={lab[0]:.3f}, a={lab[1]:.3f}, b={lab[2]:.3f}")

# Color difference between two sRGB colors
dist = hl.delta_e("#ff0000", "#00ff00")

# Perfectly uniform gradient (arc-length reparameterized)
gradient = hl.gradient("#ff0000", "#0000ff", 8)

# Ensure WCAG AA contrast (4.5:1)
adjusted = hl.ensure_contrast("#ffffff", "#3B82F6", min_ratio=4.5)

# Generate a palette (Tailwind-style 50-950 scale)
scale = hl.semantic_scale("#3B82F6")

Architecture

Helmlab is a family of purpose-built color spaces:

Helmlab (UI layer)
├── MetricSpace — 72-param enriched pipeline (distance, deltaE)
│   XYZ → M₁ → γ → M₂ → Hue → H-K → L → C → HL → NC → φ → Lab
│
└── GenSpace — generation-optimized pipeline (gradient, palette)
    XYZ → M₁ → γ=⅓ → M₂ → NC → Lab
    + CIEDE2000 arc-length reparameterization for gradient()

MetricSpace (72 parameters) is jointly optimized against COMBVD using L-BFGS-B with 8 random restarts. 13-stage enriched pipeline with hue correction, Helmholtz-Kohlrausch, chroma scaling, neutral correction, and rigid rotation.

GenSpace (18 parameters) uses CMA-ES optimized M1/M2 matrices with shared γ=⅓. No enrichment stages — pure linear-algebra pipeline, fast and invertible. Wins 28/43 perceptual benchmarks vs OKLab (6/43) and CIE Lab (9/43), with sky-blue Blue→White gradients (no purple shift).

Benchmarks

Perceptual Distance (MetricSpace)

STRESS on COMBVD (3,813 pairs). Each method uses its standard distance formula. Lower is better.

Method	COMBVD STRESS	vs CIEDE2000
Helmlab v20b	23.30	-20.1%
CIEDE2000	29.18	—
CIE94	33.59	+15.1%
CAM16-UCS (Euclid.)	33.90	+16.2%
ΔE CMC	34.04	+16.6%
IPT (Euclid.)	41.21	+41.3%
CIE Lab ΔE76	42.80	+46.7%
Oklab (Euclid.)	47.46	+62.7%

Bootstrap (10,000 iterations): Helmlab 95% CI [22.50, 23.93], CIEDE2000 95% CI [27.64, 30.84]. Zero overlap, p < 10⁻⁴.

How was STRESS measured?

STRESS (Standardized Residual Sum of Squares) is the CIE-standard metric for evaluating color difference formulas. COMBVD is a combined visual-difference dataset of 3,813 color pairs from 6 independent psychophysical experiments (Luo & Rigg 1986, RIT-DuPont, Witt, Leeds, BFD, He et al. 2022), containing 64,000+ individual human judgments. In each experiment, observers viewed color pairs under controlled D65 lighting and rated perceived differences.

For each pair i, let ΔVᵢ = human visual difference, ΔEᵢ = predicted distance. STRESS finds the optimal scale F minimizing residuals:

STRESS = 100 × √( Σ(ΔEᵢ − F·ΔVᵢ)² / Σ(ΔEᵢ)² )

Scale: 0 = perfect, 100 = no correlation. Helmlab's 72 parameters were optimized with L-BFGS-B (8 random restarts, 80/20 split, seed=42). 5-fold CV confirms generalization (mean ≈ 23.5). Full methodology: arXiv:2602.23010.

Gradient Uniformity

CV (coefficient of variation of CIEDE2000 step sizes). Lower is better.

Method	Red→Blue	Orange→Cyan	Black→White	Technique
Helmlab gradient()	≈ 0%	≈ 0%	≈ 0%	arc-length reparam.
Helmlab GenSpace	30.3%	26.5%	40.7%	linear interpolation
Oklab	31.5%	41.4%	41.2%	linear interpolation
CIE Lab	44.8%	52.3%	61.5%	linear interpolation

Note: Helmlab's gradient() achieves ≈ 0% via CIEDE2000 arc-length reparameterization — an algorithm that redistributes steps to equal perceptual spacing. Oklab and CIE Lab values reflect naive linear interpolation, which is how most libraries use them. The same reparameterization technique could be applied to any space; Helmlab ships it built-in.

Project Structure

src/helmlab/
├── helmlab.py              # Main API (Helmlab class)
├── spaces/
│   ├── metric.py           # MetricSpace — 72-param enriched pipeline
│   ├── gen.py              # GenSpace — generation-optimized pipeline
│   ├── analytical.py       # Compatibility shim → MetricSpace
│   ├── base.py             # Abstract base class
│   ├── registry.py         # Color space registry
│   └── ...                 # Baseline spaces (CAM16, IPT, Oklch, etc.)
├── metrics/
│   ├── delta_e.py          # Color difference formulas
│   ├── stress.py           # STRESS computation
│   └── benchmarks.py       # Cross-method benchmarking
├── utils/
│   ├── srgb_convert.py     # sRGB/Display P3 conversions
│   ├── gamut.py            # Gamut mapping (binary search)
│   └── ...                 # Converters, I/O, visualization
├── data/
│   ├── metric_params.json  # MetricSpace params (v20b, STRESS 23.30)
│   ├── gen_params.json     # GenSpace params (v14 CMA-ES optimized)
│   └── ...                 # Dataset loaders (COMBVD, Munsell, etc.)
├── export.py               # Token export (CSS, Android, iOS, Tailwind)
└── feedback/               # Human feedback collection tools

packages/helmlab-js/        # npm package (TypeScript)
packages/postcss-helmlab/   # PostCSS plugin
docs/                       # Documentation + interactive demo
paper/                      # LaTeX paper + figures
tests/                      # 337 tests (233 Python + 104 JavaScript)

Tests

python -m pytest tests/ -q        # 233 Python tests
cd packages/helmlab-js && npx vitest run  # 104 JS tests

Citation

@article{yildiz2025helmlab,
  title={Helmlab: A Data-Driven Analytical Color Space for UI Design Systems},
  author={Y{\i}ld{\i}z, G{\"o}rkem},
  journal={arXiv preprint arXiv:2602.23010},
  year={2025},
  url={https://arxiv.org/abs/2602.23010}
}

License

MIT