AQC0852

Nanopublication — Computational Image Analysis - AQC0852

by Arnaud Quercy · C Minor - Research on Harmony - Variation 11 · 2025

Claim 1: Computational Image Analysis - AQC0852

The artwork C Minor [1] - Research on Harmony - Variation 11 (AQC0852) [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]: 2364x3162 pixels. Analysis date: 2026-02-04.

Color Analysis

Rank Color Hex % Family Name
1 E88B74 25.6 red-orange darksalmon
2 EE9A82 21.8 red-orange lightsalmon
3 E37C63 11.6 red-orange salmon
4 C94A11 10.3 orange chocolate
5 F2AE97 8.8 orange burlywood
6 A05A70 7.7 red indianred
7 B87181 5.3 red rosybrown
8 824A61 3.9 red dusty mauve
9 3D1E18 3.0 red-orange very dark red
10 59383B 2.1 red darkslategray

Color Families:

Family %
red-orange 62.0
orange 19.0
red 19.0

Texture Analysis

Metric Value
Global Roughness 0.145
Mean Local Roughness 0.012
Roughness Uniformity 0.013
Edge Density 0.036
Mean Gradient Magnitude 0.105
Gradient Variance 0.021
Gradient Smoothness 0.0
Directional Coherence 0.021
Pattern Complexity 0.116
Pattern Repetition 1.0
Detail Frequency Ratio 0.603
Spatial Variation 0.078
Texture Consistency 0.712

Brightness & Contrast Analysis

Metric Value
Mean Brightness 0.582
Brightness Variance 0.145
Brightness Uniformity 0.752
Brightness Skewness -1.152
Brightness Entropy 6.815
Rms Contrast 0.145
Michelson Contrast 1.0
Weber Contrast 0.462
Mean Local Contrast 0.013
Contrast Uniformity 0.001
Dynamic Range 0.98
Effective Dynamic Range 0.443
Shadow Percentage 5.781
Midtone Percentage 60.719
Highlight Percentage 33.5
Shadow Clipping 0.0
Highlight Clipping 0.0
Tonal Balance 0.0
Fine Contrast 0.006
Medium Contrast 0.017
Coarse Contrast 0.028
Multiscale Contrast Ratio 0.226
Edge Contrast 0.105
Contrast Clustering 0.288

Spatial Distribution Analysis

Metric Value
Spatial Coherence 0.718
Color Clustering 0.44
Color Transition Smoothness 0.731
Transition Uniformity 0.853
Sharp Transition Ratio 0.1
Transition Directionality 0.029
Mean Saturation 0.517
Saturation Variance 0.025
Low Saturation Ratio 0.015
Medium Saturation Ratio 0.872
High Saturation Ratio 0.113
Saturation Clustering 1.0
Hue Concentration 0.972
Complementary Balance 0.0
Analogous Dominance 0.987
Temperature Bias 0.997

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). C Minor - Research on Harmony - Variation 11 — Catalog raisonné. https://arnaudquercy.art/en/catalogue-raisonne/AQC0852.html

[2] Quercy, A. (2025). Untitled - Gallery. https://artquamanima.com/en/artworks/2025/01/c-minor-research-on-harmony-variation-11_9fk.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)

711a3a92d5cbb7f71118988056d1790283fdd88aeaaf587ed79e2319fc0a5526