Computational image analysis of artwork Salut d'amour (AQC0501) [1] by Arnaud Quercy [2], performed according to IDS-CMP-2025 [3], using k-means clustering method with 10 color extraction parameters. Analysis includes color distribution, texture metrics, brightness/contrast measurements, and spatial pattern characterization. Analysis completed on 2025-10-03.
Context
Analysis performed according to IDS-CMP-2025 [3] includes four metric categories: (a) Color distribution via k-means (10 colors), (b) Texture analysis using Haralick features, (c) Brightness and contrast measurements, (d) Spatial pattern characterization. Source image: 1142x1600 pixels. Analysis date: 2025-10-03.
A) Color Analysis
| Rank |
Color |
Hex |
% |
Family |
Name |
| 1 |
|
BD8861 |
19.0 |
orange |
peru |
| 2 |
|
B07F59 |
18.2 |
orange |
indianred |
| 3 |
|
C9916A |
15.8 |
orange |
darksalmon |
| 4 |
|
D79E78 |
9.8 |
orange |
tan |
| 5 |
|
270A04 |
9.4 |
red-orange |
very dark red |
| 6 |
|
A27450 |
9.3 |
orange |
burnt sienna |
| 7 |
|
7E0F15 |
7.4 |
red-orange |
maroon |
| 8 |
|
401D16 |
6.6 |
red-orange |
very dark red |
| 9 |
|
D5C2AF |
3.9 |
orange |
silver |
| 10 |
|
563730 |
0.7 |
red-orange |
dark brown |
Color Families:
| Family |
% |
| orange |
75.9 |
| red-orange |
24.1 |
B) Texture Analysis
| Metric |
Value |
| Global Roughness |
0.206 |
| Mean Local Roughness |
0.028 |
| Roughness Uniformity |
0.023 |
| Edge Density |
0.108 |
| Mean Gradient Magnitude |
0.164 |
| Gradient Variance |
0.046 |
| Gradient Smoothness |
0.0 |
| Directional Coherence |
0.105 |
| Pattern Complexity |
0.171 |
| Pattern Repetition |
1.0 |
| Detail Frequency Ratio |
0.642 |
| Spatial Variation |
0.097 |
| Texture Consistency |
0.487 |
C) Brightness & Contrast Analysis
| Metric |
Value |
| Mean Brightness |
0.481 |
| Brightness Variance |
0.206 |
| Brightness Uniformity |
0.571 |
| Brightness Skewness |
-0.935 |
| Brightness Entropy |
6.733 |
| Rms Contrast |
0.206 |
| Michelson Contrast |
1.0 |
| Weber Contrast |
0.799 |
| Mean Local Contrast |
0.022 |
| Contrast Uniformity |
0.126 |
| Dynamic Range |
0.984 |
| Effective Dynamic Range |
0.627 |
| Shadow Percentage |
24.044 |
| Midtone Percentage |
66.096 |
| Highlight Percentage |
9.86 |
| Shadow Clipping |
0.0 |
| Highlight Clipping |
0.0 |
| Tonal Balance |
0.0 |
| Fine Contrast |
0.019 |
| Medium Contrast |
0.028 |
| Coarse Contrast |
None |
| Multiscale Contrast Ratio |
1.0 |
| Edge Contrast |
0.164 |
| Contrast Clustering |
0.513 |
D) Spatial Distribution Analysis
| Metric |
Value |
| Spatial Coherence |
0.747 |
| Color Clustering |
0.687 |
| Color Transition Smoothness |
0.589 |
| Transition Uniformity |
0.699 |
| Sharp Transition Ratio |
0.1 |
| Transition Directionality |
0.108 |
| Mean Saturation |
0.548 |
| Saturation Variance |
0.03 |
| Low Saturation Ratio |
0.039 |
| Medium Saturation Ratio |
0.777 |
| High Saturation Ratio |
0.183 |
| Saturation Clustering |
0.999 |
| Hue Concentration |
0.988 |
| Complementary Balance |
0.0 |
| Analogous Dominance |
1.0 |
| Temperature Bias |
1.0 |
Methodology
This analysis employs standardized computational methods for objective image characterization. Color extraction uses k-means clustering algorithm. Texture analysis applies Haralick feature extraction. Brightness metrics include mean, variance, and distribution analysis. Spatial patterns are characterized through coherence and clustering measurements. All methods are deterministic and reproducible. Analysis performed by Ideamorphic Studies' computational imaging systems.
