AQC0793

Nanopublication — Computational Image Analysis - AQC0793

by Arnaud Quercy · F Octaves - Reflexions 27 · 2024

Claim 1: Computational Image Analysis - AQC0793

Computational image analysis [3] of artwork F Octaves [1] - Reflexions 27 (AQC0793) [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]: 2595x3892 pixels. Analysis date: 2026-02-04.

Color Analysis

Rank Color Hex % Family Name
1 B14464 15.1 red indianred
2 D376B1 14.1 red-violet palevioletred
3 7E2A3B 12.6 red brown
4 943750 12.0 red burnt sienna
5 C55575 11.1 red lightcoral
6 DD85C3 9.9 red-violet orchid
7 201B26 9.3 violet very dark gray
8 D3CABC 6.6 yellow-orange silver
9 C4BAAD 5.2 yellow-orange steel gray
10 312E3B 4.1 violet dusty mauve
11 3B000D 0.3 red-orange very dark red [Accent]

Color Families:

Family %
red 50.8
red-violet 24.1
violet 13.4
yellow-orange 11.8
red-orange 0.3

Accent Colors:

Hex Family Name Chroma
3B000D red-orange very dark red 29.1

Texture Analysis

Metric Value
Global Roughness 0.198
Mean Local Roughness 0.015
Roughness Uniformity 0.012
Edge Density 0.062
Mean Gradient Magnitude 0.141
Gradient Variance 0.022
Gradient Smoothness 0.0
Directional Coherence 0.008
Pattern Complexity 0.111
Pattern Repetition 1.0
Detail Frequency Ratio 0.601
Spatial Variation 0.149
Texture Consistency 0.302

Brightness & Contrast Analysis

Metric Value
Mean Brightness 0.45
Brightness Variance 0.198
Brightness Uniformity 0.561
Brightness Skewness 0.045
Brightness Entropy 7.407
Rms Contrast 0.198
Michelson Contrast 1.0
Weber Contrast 0.775
Mean Local Contrast 0.018
Contrast Uniformity 0.225
Dynamic Range 1.0
Effective Dynamic Range 0.663
Shadow Percentage 30.698
Midtone Percentage 54.656
Highlight Percentage 14.646
Shadow Clipping 0.0
Highlight Clipping 0.0
Tonal Balance 0.156
Fine Contrast 0.007
Medium Contrast 0.022
Coarse Contrast 0.036
Multiscale Contrast Ratio 0.189
Edge Contrast 0.141
Contrast Clustering 0.698

Spatial Distribution Analysis

Metric Value
Spatial Coherence 0.774
Color Clustering 0.67
Color Transition Smoothness 0.647
Transition Uniformity 0.858
Sharp Transition Ratio 0.1
Transition Directionality 0.012
Mean Saturation 0.467
Saturation Variance 0.037
Low Saturation Ratio 0.219
Medium Saturation Ratio 0.728
High Saturation Ratio 0.053
Saturation Clustering 1.0
Hue Concentration 0.875
Complementary Balance 0.001
Analogous Dominance 0.884
Temperature Bias 0.878

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). F Octaves - Reflexions 27 — Catalog raisonné. https://arnaudquercy.art/en/catalogue-raisonne/AQC0793.html

[2] Quercy, A. (2025). Untitled - Gallery. https://artquamanima.com/en/artworks/2024/01/f-octaves-reflexions-27_8sm.html

[3] Quercy, A. (2025). Computational Image Analysis Standard - MMIDS-CMP-2025 https://multimodal.institute/en/publications/2025/10/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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