AQC0944

Nanopublication — Computational Image Analysis - AQC0944

Claim 1: Computational Image Analysis - AQC0944

The artwork C Minor [1] - Research on Harmony - Variations 15 (AQC0944) [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]: 1870x2618 pixels. Analysis date: 2026-02-04.

Color Analysis

Rank Color Hex % Family Name
1 88A1F6 20.2 violet cornflowerblue
2 D5B78E 15.6 yellow-orange tan
3 0E0B10 12.2 black black
4 3070D3 11.7 blue-violet royalblue
5 E5E4E0 10.1 white white
6 E3B8C2 9.4 red thistle
7 BF6813 8.4 orange chocolate
8 901422 4.6 red-orange brown
9 B49D7F 4.5 yellow-orange rosybrown
10 1F2034 3.3 violet very dark gray
11 693A5E 0.3 red-violet dusty mauve [Accent]

Color Families:

Family %
violet 23.5
yellow-orange 20.1
black 12.2
blue-violet 11.7
white 10.1
red 9.4
orange 8.4
red-orange 4.6
red-violet 0.3

Accent Colors:

Hex Family Name Chroma
693A5E red-violet dusty mauve 30.0

Texture Analysis

Metric Value
Global Roughness 0.263
Mean Local Roughness 0.014
Roughness Uniformity 0.016
Edge Density 0.03
Mean Gradient Magnitude 0.111
Gradient Variance 0.042
Gradient Smoothness 0.0
Directional Coherence 0.003
Pattern Complexity 0.12
Pattern Repetition 1.0
Detail Frequency Ratio 0.592
Spatial Variation 0.164
Texture Consistency 0.579

Brightness & Contrast Analysis

Metric Value
Mean Brightness 0.546
Brightness Variance 0.263
Brightness Uniformity 0.519
Brightness Skewness -0.669
Brightness Entropy 7.324
Rms Contrast 0.263
Michelson Contrast 1.0
Weber Contrast 0.919
Mean Local Contrast 0.015
Contrast Uniformity 0.0
Dynamic Range 1.0
Effective Dynamic Range 0.855
Shadow Percentage 20.013
Midtone Percentage 40.654
Highlight Percentage 39.333
Shadow Clipping 0.0
Highlight Clipping 0.001
Tonal Balance 0.017
Fine Contrast 0.008
Medium Contrast 0.019
Coarse Contrast 0.03
Multiscale Contrast Ratio 0.259
Edge Contrast 0.111
Contrast Clustering 0.421

Spatial Distribution Analysis

Metric Value
Spatial Coherence 0.79
Color Clustering 0.658
Color Transition Smoothness 0.708
Transition Uniformity 0.695
Sharp Transition Ratio 0.1
Transition Directionality 0.005
Mean Saturation 0.445
Saturation Variance 0.074
Low Saturation Ratio 0.316
Medium Saturation Ratio 0.43
High Saturation Ratio 0.254
Saturation Clustering 0.999
Hue Concentration 0.241
Complementary Balance 0.155
Analogous Dominance 0.497
Temperature Bias 0.069

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 - Variations 15 — Catalog raisonné. https://arnaudquercy.art/en/catalogue-raisonne/AQC0944.html

[2] Quercy, A. (2025). Untitled - Gallery. https://artquamanima.com/en/artworks/2025/12/c-minor-research-on-harmony-variations-15_1i5i.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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