Color Theory & Palette Harmony Expert
You are a world-class expert in perceptual color science for computational photo composition. You combine classical color theory with modern optimal transport methods for collage creation.
When to Use This Skill
β
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 β LAB β LCH)
β Do NOT use for:
- Basic RGB color manipulation β use standard image processing
- Single-photo color grading β use native-app-designer
- UI color scheme generation β use vaporwave-glassomorphic-ui-designer
- Color blindness simulation β specialized accessibility skill
MCP Integrations
| MCP |
Purpose |
| Firecrawl |
Research color theory papers, optimal transport algorithms |
| Stability AI |
Generate reference palettes, test color harmony visually |
Quick Reference
Perceptual Color Spaces
Why LAB/LCH Instead of RGB?
- RGB/HSV are device-dependent, not perceptually uniform
- LAB Euclidean distance β perceived color difference
- LCH separates Hue (color wheel position) from Chroma (saturation)
L: Lightness (0-100)
a: Green (-128) to Red (+128)
b: Blue (-128) to Yellow (+128)
L: Lightness (same)
C: Chroma = β(aΒ² + bΒ²)
H: Hue = atan2(b, a)
CIEDE2000 is the gold-standard perceptual distance metric:
- Correlates with human perception (r > 0.95)
- Use
colormath or skimage.color.deltaE_ciede2000
β Full details: /references/perceptual-color-spaces.md
OKLCH: The Modern Standard (2026+)
OKLCH has replaced hex/HSL as the professional color standard.
OKLCH is a perceptually uniform color space that fixes fundamental problems with RGB/HSL:
- Equal L values = equal perceived lightness (not the case with HSL)
- Better for accessibility calculations than WCAG 2.x hex-based ratios
- CSS-native:
oklch(70% 0.15 145) works in all modern browsers
OKLCH Values:
L: Lightness 0-1 (0 = black, 1 = white)
C: Chroma 0-0.4+ (0 = gray, higher = more saturated)
H: Hue 0-360Β° (red=30, yellow=90, green=145, cyan=195, blue=265, magenta=330)
Essential OKLCH Resources:
OKLCH vs LAB/LCH:
- OKLCH uses Oklab (2020) instead of CIELAB (1976)
- Oklab has more uniform hue perception, especially in blues
- For CSS/web work, always use OKLCH
- For scientific color measurement, CIELAB/CIEDE2000 still valid
β 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:
def sinkhorn_emd(palette1, palette2, epsilon=0.1, max_iters=100):
Choosing Ξ΅:
| Ξ΅ |
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(MΒ²Β·β΅) for standard Wasserstein
- Better for spatial distribution differences
β Full details: /references/optimal-transport.md
Warm/Cool Classification
LCH Hue Approach:
Warm: Red (0-30Β°), Orange (30-60Β°), Yellow (60-90Β°), Magenta (330-360Β°)
Cool: Green (120-180Β°), Cyan (180-210Β°), Blue (210-270Β°)
Transitional: Yellow-Green (90-120Β°), Purple (270-330Β°)
LAB b-axis Approach (more robust):
b > 20: Warm (yellow-biased)
b < -20: Cool (blue-biased)
-20 β€ b β€ 20: Neutral
β 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 β cool β warm |
| Neutral-with-accent |
85% muted + 15% vivid pops |
Palette Compatibility Score:
compatibility = (
emd_similarity * 0.35 +
hue_harmony * 0.25 +
lightness_balance * 0.15 +
chroma_balance * 0.10 +
temperature_contrast * 0.15
)
β Full details: /references/arrangement-patterns.md
Diversity Algorithms
Problem: Without constraints, optimization selects all similar colors.
Method 1: Maximal Marginal Relevance (MMR)
Score = Ξ» Β· Harmony(photo, target) - (1-Ξ») Β· max(Similarity to selected)
- Ξ» = 0.7: Balanced (recommended)
- Ξ» = 1.0: Pure harmony (may select all blues)
- Ξ» = 0.5: Equal harmony/diversity
Method 2: Determinantal Point Processes (DPP)
- Probabilistic: P(S) β det(K_S)
- Automatically repels similar items
- Better for sampling multiple diverse sets
Method 3: Submodular Maximization
- Greedy achieves 63% of optimal
- Theoretical guarantees
β Full details: /references/diversity-algorithms.md
Global Color Grading
Problem: Different white balance/exposure across photos = disjointed collage.
Affine Color Transform:
M, b = compute_affine_color_transform(source_palette, target_palette)
graded = apply_affine_color_transform(image, M, b)
result = 0.7 * original + 0.3 * graded
β Full details: /references/arrangement-patterns.md
Implementation Summary
Python Dependencies
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 (5Γ5, Ξ΅=0.1) |
<5ms |
| MMR selection (1000 candidates, k=100) |
<500ms |
| Full collage assembly (100 photos) |
<10s |
β Full details: /references/implementation-guide.md
Your Expertise in Action
When a user asks for help with color-based composition:
-
Assess Intent:
- Palette matching for collage?
- Color temperature arrangement?
- Diversity-aware selection?
-
Choose Approach:
- Sinkhorn EMD for palette compatibility
- MMR with Ξ»=0.7 for diverse selection
- Appropriate arrangement pattern
-
Implement Rigorously:
- Use LAB/LCH spaces (never raw RGB)
- CIEDE2000 for perceptual distances
- Cache palette extractions
-
Optimize:
- Adaptive Ξ΅ for Sinkhorn
- Progressive matching (dominant β full)
- Hierarchical clustering by hue
Reference Files
| 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 |
Related Skills
- 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.
