AQC0726

Nanopublication — Computational Image Analysis - AQC0726

Claim 1: Computational Image Analysis - AQC0726

The artwork Db Minor [1] - Research on Harmony - Variation 5 (AQC0726) [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]: 3024x4032 pixels. Analysis date: 2026-02-04.

Color Analysis

Rank Color Hex % Family Name
1 D1B496 18.4 orange tan
2 363F5A 17.5 blue-violet grayish purple
3 66A4AE 12.8 blue-green cadetblue
4 E6CB94 10.6 yellow-orange burlywood
5 2F292C 10.4 gray very dark gray
6 C9B075 8.8 yellow-orange ochre
7 E2DBD0 7.6 yellow-orange gainsboro
8 C9C7C1 6.9 white silver
9 B29720 3.8 yellow-orange darkgoldenrod
10 425F8A 3.0 blue-violet grayish purple
11 2C1109 0.3 red-orange very dark red [Accent]
12 09161C 0.3 blue black [Accent]
13 8D9D49 0.3 yellow-green olivedrab [Accent]
14 C48699 0.3 red rosybrown [Accent]
15 0D143A 0.3 violet very dark purple [Accent]

Color Families:

Family %
yellow-orange 30.9
blue-violet 20.5
orange 18.4
blue-green 12.8
gray 10.4
white 6.9
red-orange 0.3
blue 0.3
yellow-green 0.3
red 0.3
violet 0.3

Accent Colors:

Hex Family Name Chroma
2C1109 red-orange very dark red 15.8
09161C blue black 7.3
8D9D49 yellow-green olivedrab 45.2
C48699 red rosybrown 27.0
0D143A violet very dark purple 28.6

Texture Analysis

Metric Value
Global Roughness 0.243
Mean Local Roughness 0.021
Roughness Uniformity 0.021
Edge Density 0.1
Mean Gradient Magnitude 0.166
Gradient Variance 0.051
Gradient Smoothness 0.0
Directional Coherence 0.016
Pattern Complexity 0.121
Pattern Repetition 1.0
Detail Frequency Ratio 0.636
Spatial Variation 0.182
Texture Consistency 0.507

Brightness & Contrast Analysis

Metric Value
Mean Brightness 0.57
Brightness Variance 0.243
Brightness Uniformity 0.574
Brightness Skewness -0.536
Brightness Entropy 7.258
Rms Contrast 0.243
Michelson Contrast 1.0
Weber Contrast 0.744
Mean Local Contrast 0.023
Contrast Uniformity 0.028
Dynamic Range 1.0
Effective Dynamic Range 0.686
Shadow Percentage 28.913
Midtone Percentage 20.688
Highlight Percentage 50.399
Shadow Clipping 0.004
Highlight Clipping 0.01
Tonal Balance 0.0
Fine Contrast 0.011
Medium Contrast 0.028
Coarse Contrast 0.04
Multiscale Contrast Ratio 0.278
Edge Contrast 0.166
Contrast Clustering 0.493

Spatial Distribution Analysis

Metric Value
Spatial Coherence 0.752
Color Clustering 0.697
Color Transition Smoothness 0.577
Transition Uniformity 0.657
Sharp Transition Ratio 0.1
Transition Directionality 0.02
Mean Saturation 0.327
Saturation Variance 0.034
Low Saturation Ratio 0.453
Medium Saturation Ratio 0.51
High Saturation Ratio 0.037
Saturation Clustering 0.999
Hue Concentration 0.167
Complementary Balance 0.269
Analogous Dominance 0.56
Temperature Bias 0.158

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 5 — Catalog raisonné. https://arnaudquercy.art/en/catalogue-raisonne/AQC0726.html

[2] Quercy, A. (2025). Untitled - Gallery. https://artquamanima.com/en/artworks/2024/01/db-minor-research-on-harmony-variation-5_82k.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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