AQC0579

Nanopublication — Computational Image Analysis - AQC0579

by Arnaud Quercy · Reader Of Parc Monceau, Paris · 2024

Claim 1: Computational Image Analysis - AQC0579

Computational image analysis [3] of artwork Reader [1] Of Parc Monceau, Paris (AQC0579) [2] by Arnaud Quercy [2] 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 2026-02-04.

Context

Analysis performed according to MMIDS-CMP-2025 [3] includes four metric categories: (1) Color distribution via k-means (10 colors), (2) Texture analysis using Haralick features, (3) Brightness and contrast measurements, (4) Spatial pattern characterization. Source image [5]: 2727x2727 pixels. Analysis date: 2026-02-04.

Color Analysis

Rank Color Hex % Family Name
1 6E6457 18.5 yellow-orange dimgray
2 857766 15.3 yellow-orange gray
3 595044 15.0 yellow-orange dark brown
4 3E372B 13.8 yellow-orange darkslategray
5 988E7F 12.3 yellow-orange grey
6 8D5E25 7.9 orange burnt sienna
7 B4A895 6.6 yellow-orange steel gray
8 D5CCB7 3.9 yellow-orange silver
9 C6945E 3.9 orange peru
10 190F0A 2.7 orange black
11 D7918B 0.3 red-orange darksalmon [Accent]

Color Families:

Family %
yellow-orange 85.5
orange 14.5
red-orange 0.3

Accent Colors:

Hex Family Name Chroma
D7918B red-orange darksalmon 29.5

Texture Analysis

Metric Value
Global Roughness 0.163
Mean Local Roughness 0.034
Roughness Uniformity 0.026
Edge Density 0.197
Mean Gradient Magnitude 0.261
Gradient Variance 0.08
Gradient Smoothness 0.0
Directional Coherence 0.02
Pattern Complexity 0.123
Pattern Repetition 1.0
Detail Frequency Ratio 0.655
Spatial Variation 0.069
Texture Consistency 0.658

Brightness & Contrast Analysis

Metric Value
Mean Brightness 0.428
Brightness Variance 0.163
Brightness Uniformity 0.619
Brightness Skewness 0.244
Brightness Entropy 7.365
Rms Contrast 0.163
Michelson Contrast 1.0
Weber Contrast 0.65
Mean Local Contrast 0.036
Contrast Uniformity 0.265
Dynamic Range 1.0
Effective Dynamic Range 0.533
Shadow Percentage 26.178
Midtone Percentage 65.846
Highlight Percentage 7.976
Shadow Clipping 0.028
Highlight Clipping 0.004
Tonal Balance 0.07
Fine Contrast 0.02
Medium Contrast 0.045
Coarse Contrast 0.064
Multiscale Contrast Ratio 0.315
Edge Contrast 0.261
Contrast Clustering 0.342

Spatial Distribution Analysis

Metric Value
Spatial Coherence 0.721
Color Clustering 0.647
Color Transition Smoothness 0.312
Transition Uniformity 0.449
Sharp Transition Ratio 0.1
Transition Directionality 0.021
Mean Saturation 0.285
Saturation Variance 0.042
Low Saturation Ratio 0.651
Medium Saturation Ratio 0.271
High Saturation Ratio 0.077
Saturation Clustering 0.998
Hue Concentration 0.986
Complementary Balance 0.0
Analogous Dominance 0.999
Temperature Bias 0.991

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 Multimodal Institute's computational imaging systems.

References

[1] Arnaud Quercy (2024). Reader Of Parc Monceau, Paris — Catalog raisonné. https://arnaudquercy.art/en/catalogue-raisonne/AQC0579.html

[2] Quercy, A. (2025). Untitled - Gallery. https://artquamanima.com/en/artworks/2024/01/reader-of-parc-monceau-paris_6he.html

[3] Quercy, A. (2025). Computational Image Analysis Standard - MMIDS-CMP-2025 https://multimodal.institute/en/publications/2025/11/mmids-cmp-2025-computational-image-analysis-standard-dg1.html

Epistemic profile

Claim typecomputational analysis
Voicethird person
Epistemic statusempirical measurement
Methodologycomputational analysis
Certaintyhigh

Checksum (SHA-256)

c3aac4d52e3cb8e79b6de3909d7afad0e61169bbe4c0c793038064a02d680444