AQC0913

Nanopublication — Computational Image Analysis - AQC0913

by Arnaud Quercy · G Major - Research on Harmony - Variations 12 · 2025

Claim 1: Computational Image Analysis - AQC0913

The artwork G Major [1] - Research on Harmony - Variations 12 (AQC0913) [2] by Arnaud Quercy [2] underwent comprehensive computational analysis [3] on 2025-12-11. 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]: 1936x1936 pixels. Analysis date: 2025-12-11.

Color Analysis

Rank Color Hex % Family Name
1 E59A54 24.2 orange sandybrown
2 E8833A 21.6 orange peru
3 EFAE6A 13.2 orange lightsalmon
4 5B5349 12.6 yellow-orange dark brown
5 686056 11.0 yellow-orange dimgray
6 E97C0D 6.1 orange darkorange
7 B8BF59 4.4 yellow ochre
8 F5E5D6 3.6 orange white
9 E1CDBD 2.3 orange lightgray
10 562C17 1.0 orange russet
11 400C06 0.3 red-orange very dark red [Accent]

Color Families:

Family %
orange 72.0
yellow-orange 23.6
yellow 4.4
red-orange 0.3

Accent Colors:

Hex Family Name Chroma
400C06 red-orange very dark red 29.7

Texture Analysis

Metric Value
Global Roughness 0.158
Mean Local Roughness 0.019
Roughness Uniformity 0.02
Edge Density 0.068
Mean Gradient Magnitude 0.152
Gradient Variance 0.043
Gradient Smoothness 0.0
Directional Coherence 0.025
Pattern Complexity 0.113
Pattern Repetition 1.0
Detail Frequency Ratio 0.64
Spatial Variation 0.121
Texture Consistency 0.553

Brightness & Contrast Analysis

Metric Value
Mean Brightness 0.59
Brightness Variance 0.158
Brightness Uniformity 0.733
Brightness Skewness -0.434
Brightness Entropy 6.816
Rms Contrast 0.158
Michelson Contrast 0.992
Weber Contrast 0.534
Mean Local Contrast 0.022
Contrast Uniformity 0.026
Dynamic Range 0.996
Effective Dynamic Range 0.49
Shadow Percentage 5.958
Midtone Percentage 57.982
Highlight Percentage 36.06
Shadow Clipping 0.0
Highlight Clipping 0.0
Tonal Balance 0.0
Fine Contrast 0.01
Medium Contrast 0.026
Coarse Contrast 0.034
Multiscale Contrast Ratio 0.279
Edge Contrast 0.152
Contrast Clustering 0.447

Spatial Distribution Analysis

Metric Value
Spatial Coherence 0.764
Color Clustering 0.323
Color Transition Smoothness 0.621
Transition Uniformity 0.723
Sharp Transition Ratio 0.1
Transition Directionality 0.035
Mean Saturation 0.527
Saturation Variance 0.063
Low Saturation Ratio 0.29
Medium Saturation Ratio 0.436
High Saturation Ratio 0.273
Saturation Clustering 0.999
Hue Concentration 0.987
Complementary Balance 0.0
Analogous Dominance 1.0
Temperature Bias 0.953

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 (2025). G Major - Research on Harmony - Variations 12 — Catalog raisonné. https://arnaudquercy.art/en/catalogue-raisonne/AQC0913.html

[2] Quercy, A. (2025). Untitled - Gallery. https://artquamanima.com/en/artworks/2025/11/g-major-research-on-harmony-variations-12_ieo.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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