AQC0570

Nanopublication — Computational Image Analysis - AQC0570

by Arnaud Quercy · Composition 4 · 2024

Claim 1: Computational Image Analysis - AQC0570

The artwork Composition [1] 4 (AQC0570) [2] by Arnaud Quercy [2] underwent comprehensive computational analysis [3] on 2026-02-04. Method: k-means clustering with 10 colors extracted. Metrics documented: color distribution, texture analysis, brightness/contrast, spatial patterns.

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]: 526x789 pixels. Analysis date: 2026-02-04.

Color Analysis

Rank Color Hex % Family Name
1 292D32 14.6 gray very dark gray
2 9B9179 12.6 yellow-orange gray
3 8A795B 10.8 yellow-orange grey
4 775C3C 10.8 orange dark brown
5 3A5D63 10.3 blue-green darkslategray
6 D7D76D 9.6 yellow burlywood
7 AFA995 9.2 yellow steel gray
8 5E4122 7.9 orange dark brown
9 CAD1C3 7.8 yellow-green lightgray
10 B4B54C 6.4 yellow ochre
11 518683 0.3 green blue gray [Accent]
12 658491 0.3 blue blue gray [Accent]

Color Families:

Family %
yellow 25.2
yellow-orange 23.4
orange 18.7
gray 14.6
blue-green 10.3
yellow-green 7.8
green 0.3
blue 0.3

Accent Colors:

Hex Family Name Chroma
518683 green blue gray 18.4
658491 blue blue gray 13.6

Texture Analysis

Metric Value
Global Roughness 0.215
Mean Local Roughness 0.047
Roughness Uniformity 0.024
Edge Density 0.232
Mean Gradient Magnitude 0.262
Gradient Variance 0.051
Gradient Smoothness 0.135
Directional Coherence 0.013
Pattern Complexity 0.15
Pattern Repetition 1.0
Detail Frequency Ratio 0.653
Spatial Variation 0.133
Texture Consistency 0.675

Brightness & Contrast Analysis

Metric Value
Mean Brightness 0.489
Brightness Variance 0.215
Brightness Uniformity 0.561
Brightness Skewness 0.037
Brightness Entropy 7.63
Rms Contrast 0.215
Michelson Contrast 1.0
Weber Contrast 0.757
Mean Local Contrast 0.04
Contrast Uniformity 0.511
Dynamic Range 0.996
Effective Dynamic Range 0.667
Shadow Percentage 28.568
Midtone Percentage 46.601
Highlight Percentage 24.831
Shadow Clipping 0.0
Highlight Clipping 0.0
Tonal Balance 0.394
Fine Contrast 0.04
Medium Contrast 0.05
Coarse Contrast 0.057
Multiscale Contrast Ratio 0.712
Edge Contrast 0.262
Contrast Clustering 0.325

Spatial Distribution Analysis

Metric Value
Spatial Coherence 0.743
Color Clustering 0.689
Color Transition Smoothness 0.315
Transition Uniformity 0.657
Sharp Transition Ratio 0.1
Transition Directionality 0.012
Mean Saturation 0.348
Saturation Variance 0.04
Low Saturation Ratio 0.46
Medium Saturation Ratio 0.514
High Saturation Ratio 0.027
Saturation Clustering 0.998
Hue Concentration 0.526
Complementary Balance 0.104
Analogous Dominance 0.756
Temperature Bias 0.341

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). Composition 4 — Catalog raisonné. https://arnaudquercy.art/en/catalogue-raisonne/AQC0570.html

[2] Quercy, A. (2025). Untitled - Gallery. https://artquamanima.com/en/artworks/2024/01/composition-4_6dw.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)

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