AQC0585

Nanopublication — Computational Image Analysis - AQC0585

by Arnaud Quercy · C minor - Research on Harmony - Variation 2 · 2024

Claim 1: Computational Image Analysis - AQC0585

The artwork C minor - Research [1] on Harmony - Variation 2 (AQC0585) [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]: 2717x3623 pixels. Analysis date: 2026-02-04.

Color Analysis

Rank Color Hex % Family Name
1 E11E09 32.5 red-orange red
2 E8301A 14.3 red-orange orangered
3 C42F28 11.5 red-orange firebrick
4 5A1D44 10.5 red-violet dusty mauve
5 DC4E46 10.3 red-orange indianred
6 7A5C7B 7.1 red-violet dusty mauve
7 F1776F 5.5 red-orange salmon
8 B3949A 3.7 red rosybrown
9 260C0F 2.5 red-orange very dark gray
10 EFBDAD 2.1 red-orange lightpink
11 E0610F 0.3 orange chocolate [Accent]

Color Families:

Family %
red-orange 78.8
red-violet 17.6
red 3.7
orange 0.3

Accent Colors:

Hex Family Name Chroma
E0610F orange chocolate 77.2

Texture Analysis

Metric Value
Global Roughness 0.131
Mean Local Roughness 0.022
Roughness Uniformity 0.026
Edge Density 0.099
Mean Gradient Magnitude 0.189
Gradient Variance 0.071
Gradient Smoothness 0.0
Directional Coherence 0.063
Pattern Complexity 0.124
Pattern Repetition 1.0
Detail Frequency Ratio 0.636
Spatial Variation 0.068
Texture Consistency 0.546

Brightness & Contrast Analysis

Metric Value
Mean Brightness 0.382
Brightness Variance 0.131
Brightness Uniformity 0.657
Brightness Skewness 0.747
Brightness Entropy 6.727
Rms Contrast 0.131
Michelson Contrast 1.0
Weber Contrast 0.611
Mean Local Contrast 0.025
Contrast Uniformity 0.0
Dynamic Range 1.0
Effective Dynamic Range 0.459
Shadow Percentage 28.152
Midtone Percentage 68.101
Highlight Percentage 3.748
Shadow Clipping 0.001
Highlight Clipping 0.001
Tonal Balance 0.0
Fine Contrast 0.013
Medium Contrast 0.031
Coarse Contrast None
Multiscale Contrast Ratio 1.0
Edge Contrast 0.189
Contrast Clustering 0.454

Spatial Distribution Analysis

Metric Value
Spatial Coherence 0.771
Color Clustering 0.514
Color Transition Smoothness 0.572
Transition Uniformity 0.604
Sharp Transition Ratio 0.1
Transition Directionality 0.064
Mean Saturation 0.749
Saturation Variance 0.058
Low Saturation Ratio 0.09
Medium Saturation Ratio 0.251
High Saturation Ratio 0.659
Saturation Clustering 0.999
Hue Concentration 0.948
Complementary Balance 0.0
Analogous Dominance 0.978
Temperature Bias 0.972

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). C minor - Research on Harmony - Variation 2 — Catalog raisonné. https://arnaudquercy.art/en/catalogue-raisonne/AQC0585.html

[2] Quercy, A. (2025). Untitled - Gallery. https://artquamanima.com/en/artworks/2024/01/c-minor-research-on-harmony-variation-2_6jq.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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