color-theory-palette-harmony-expert

✅ Use for:

• Palette-based photo selection for collages
• Warm/cool color alternation algorithms
• Hue-sorted photo sequences (rainbow gradients)
• Palette compatibility using earth-mover distance
• Diversity penalties to avoid color monotony
• Global color harmony across photo collections
• Neutral-with-splash-of-color patterns
• Perceptual color space transformations (RGB → LAB → LCH)

❌ Do NOT use for:

• Basic RGB color manipulation → use standard image processing
• Single-photo color grading → use native-app-designer
• UI color scheme generation → use vaporwave-glassomorphic-ui-designer
• Color blindness simulation → specialized accessibility skill

| MCP | Purpose |

|-----|---------|

| Firecrawl | Research color theory papers, optimal transport algorithms |

| Stability AI | Generate reference palettes, test color harmony visually |

---

Perceptual Color Spaces

Why LAB/LCH Instead of RGB?

• RGB/HSV are device-dependent, not perceptually uniform
• LAB Euclidean distance ≈ perceived color difference
• LCH separates Hue (color wheel position) from Chroma (saturation)

```python

# CIELAB (LAB) Space

L: Lightness (0-100)

a: Green (-128) to Red (+128)

b: Blue (-128) to Yellow (+128)

# CIE LCH (Cylindrical)

L: Lightness (same)

C: Chroma = √(a² + b²) # Colorfulness

H: Hue = atan2(b, a) # Angle 0-360°

```

CIEDE2000 is the gold-standard perceptual distance metric:

• Correlates with human perception (r > 0.95)
• Use colormath or skimage.color.deltaE_ciede2000

→ Full details: /references/perceptual-color-spaces.md

---

Earth-Mover Distance (Wasserstein)

Problem: How different are two photo color distributions perceptually?

Sinkhorn Algorithm - Fast O(NM) entropic EMD:

```python

def sinkhorn_emd(palette1, palette2, epsilon=0.1, max_iters=100):

# Kernel K = exp(-CostMatrix / epsilon)

# Iterate: u = a / (K @ v), v = b / (K.T @ u)

# EMD = sqrt(sum(gamma * Cost))

```

Choosing ε:

| ε | Accuracy | Speed |

|---|----------|-------|

| 0.01 | Nearly exact | 50-100 iters |

| 0.1 | Good (recommended) | 10-20 iters |

| 1.0 | Very rough | <5 iters |

Multiscale Sliced Wasserstein (2024):

• O(M log M) vs O(M²·⁵) for standard Wasserstein
• Better for spatial distribution differences

→ Full details: /references/optimal-transport.md

---

Warm/Cool Classification

LCH Hue Approach:

```

Warm: Red (0-30°), Orange (30-60°), Yellow (60-90°), Magenta (330-360°)

Cool: Green (120-180°), Cyan (180-210°), Blue (210-270°)

Transitional: Yellow-Green (90-120°), Purple (270-330°)

```

LAB b-axis Approach (more robust):

```

b > 20: Warm (yellow-biased)

b < -20: Cool (blue-biased)

-20 ≤ b ≤ 20: Neutral

```

→ Full details: /references/temperature-classification.md

---

Arrangement Patterns

| Pattern | Description |

|---------|-------------|

| Hue-sorted | Rainbow gradient, circular mean handling |

| Warm/cool alternation | Visual rhythm, prevent monotony |

| Temperature wave | Sinusoidal warm → cool → warm |

| Neutral-with-accent | 85% muted + 15% vivid pops |

Palette Compatibility Score:

```python

compatibility = (

emd_similarity * 0.35 +

hue_harmony * 0.25 + # Complementary, analogous, triadic

lightness_balance * 0.15 +

chroma_balance * 0.10 +

temperature_contrast * 0.15

)

```

→ Full details: /references/arrangement-patterns.md

---

Diversity Algorithms

Problem: Without constraints, optimization selects all similar colors.

Method 1: Maximal Marginal Relevance (MMR)

```

Score = λ · Harmony(photo, target) - (1-λ) · max(Similarity to selected)

```

• λ = 0.7: Balanced (recommended)
• λ = 1.0: Pure harmony (may select all blues)
• λ = 0.5: Equal harmony/diversity

Method 2: Determinantal Point Processes (DPP)

• Probabilistic: P(S) ∝ det(K_S)
• Automatically repels similar items
• Better for sampling multiple diverse sets

Method 3: Submodular Maximization

• Greedy achieves 63% of optimal
• Theoretical guarantees

→ Full details: /references/diversity-algorithms.md

---

Global Color Grading

Problem: Different white balance/exposure across photos = disjointed collage.

Affine Color Transform:

```python

# Find M, b where transformed = M @ LAB_color + b

M, b = compute_affine_color_transform(source_palette, target_palette)

graded = apply_affine_color_transform(image, M, b)

# Blend subtly (30% correction)

result = 0.7 original + 0.3 graded

```

→ Full details: /references/arrangement-patterns.md

---

Python Dependencies

```bash

pip install colormath opencv-python numpy scipy scikit-image pot hnswlib

```

| Package | Purpose |

|---------|---------|

| colormath | CIEDE2000, LAB/LCH conversions |

| pot | Python Optimal Transport |

| scikit-image | deltaE calculations |

Performance Targets

| Operation | Target |

|-----------|--------|

| Palette extraction (5 colors) | <50ms |

| Sinkhorn EMD (5×5, ε=0.1) | <5ms |

| MMR selection (1000 candidates, k=100) | <500ms |

| Full collage assembly (100 photos) | <10s |

→ Full details: /references/implementation-guide.md

---

When a user asks for help with color-based composition:

1. Assess Intent:

- Palette matching for collage?

- Color temperature arrangement?

- Diversity-aware selection?

1. Choose Approach:

- Sinkhorn EMD for palette compatibility

- MMR with λ=0.7 for diverse selection

- Appropriate arrangement pattern

1. Implement Rigorously:

- Use LAB/LCH spaces (never raw RGB)

- CIEDE2000 for perceptual distances

- Cache palette extractions

1. Optimize:

- Adaptive ε for Sinkhorn

- Progressive matching (dominant → full)

- Hierarchical clustering by hue

---

| File | Content |

|------|---------|

| /references/perceptual-color-spaces.md | LAB, LCH, CIEDE2000, conversions |

| /references/optimal-transport.md | EMD, Sinkhorn, MS-SWD algorithms |

| /references/temperature-classification.md | Warm/cool, hue sorting, alternation |

| /references/arrangement-patterns.md | Neutral-accent, compatibility, grading |

| /references/diversity-algorithms.md | MMR, DPP, submodular maximization |

| /references/implementation-guide.md | Python deps, Metal shaders, caching |

---

• collage-layout-expert - Color harmonization for collages
• design-system-creator - Color tokens in design systems
• vaporwave-glassomorphic-ui-designer - UI color palettes
• photo-composition-critic - Aesthetic scoring

---

Where perceptual color science meets computational composition.