AQC0750

Nanopublication — Computational Image Analysis - AQC0750

Claim 1: Computational Image Analysis - AQC0750

The artwork Db Minor [1] - Research on Harmony - Variation 6 (AQC0750) [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]: 2953x3937 pixels. Analysis date: 2026-02-04.

Color Analysis

Rank Color Hex % Family Name
1 181922 24.1 violet black
2 BDB19A 14.8 yellow-orange tan
3 EAD5BD 14.2 yellow-orange wheat
4 54AACB 11.3 blue mediumturquoise
5 A8C7D1 9.9 blue lightsteelblue
6 373741 9.2 violet dusty mauve
7 2C80BF 8.0 blue-violet steelblue
8 1C44A7 5.3 violet darkslateblue
9 E6BD81 2.0 yellow-orange burlywood
10 878E48 1.2 yellow olivedrab
11 452215 0.3 orange very dark orange [Accent]
12 421C18 0.3 red-orange very dark red [Accent]
13 619C82 0.3 yellow-green cadetblue [Accent]
14 40727F 0.3 blue-green dimgray [Accent]
15 8EACA8 0.3 green steel gray [Accent]

Color Families:

Family %
violet 38.6
yellow-orange 31.0
blue 21.1
blue-violet 8.0
yellow 1.2
orange 0.3
red-orange 0.3
yellow-green 0.3
blue-green 0.3
green 0.3

Accent Colors:

Hex Family Name Chroma
452215 orange very dark orange 22.6
421C18 red-orange very dark red 21.1
619C82 yellow-green cadetblue 26.2
40727F blue-green dimgray 17.7
8EACA8 green steel gray 11.0

Texture Analysis

Metric Value
Global Roughness 0.285
Mean Local Roughness 0.01
Roughness Uniformity 0.011
Edge Density 0.025
Mean Gradient Magnitude 0.098
Gradient Variance 0.024
Gradient Smoothness 0.0
Directional Coherence 0.028
Pattern Complexity 0.12
Pattern Repetition 1.0
Detail Frequency Ratio 0.575
Spatial Variation 0.234
Texture Consistency 0.537

Brightness & Contrast Analysis

Metric Value
Mean Brightness 0.48
Brightness Variance 0.285
Brightness Uniformity 0.407
Brightness Skewness -0.153
Brightness Entropy 7.438
Rms Contrast 0.285
Michelson Contrast 1.0
Weber Contrast 0.884
Mean Local Contrast 0.012
Contrast Uniformity 0.0
Dynamic Range 1.0
Effective Dynamic Range 0.78
Shadow Percentage 37.817
Midtone Percentage 23.009
Highlight Percentage 39.174
Shadow Clipping 0.002
Highlight Clipping 0.0
Tonal Balance 0.115
Fine Contrast 0.005
Medium Contrast 0.015
Coarse Contrast 0.028
Multiscale Contrast Ratio 0.168
Edge Contrast 0.098
Contrast Clustering 0.463

Spatial Distribution Analysis

Metric Value
Spatial Coherence 0.781
Color Clustering 0.745
Color Transition Smoothness 0.734
Transition Uniformity 0.83
Sharp Transition Ratio 0.1
Transition Directionality 0.04
Mean Saturation 0.352
Saturation Variance 0.054
Low Saturation Ratio 0.6
Medium Saturation Ratio 0.257
High Saturation Ratio 0.143
Saturation Clustering 0.999
Hue Concentration 0.559
Complementary Balance 0.136
Analogous Dominance 0.793
Temperature Bias -0.504

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). Db Minor - Research on Harmony - Variation 6 — Catalog raisonné. https://arnaudquercy.art/en/catalogue-raisonne/AQC0750.html

[2] Quercy, A. (2025). Untitled - Gallery. https://artquamanima.com/en/artworks/2024/01/db-minor-research-on-harmony-variation-6_8bw.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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