AQC0691

Nanopublication — Computational Image Analysis - AQC0691

Claim 1: Computational Image Analysis - AQC0691

The artwork Ab minor - Research [1] on Harmony - Variation 4 (AQC0691) [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]: 2281x2281 pixels. Analysis date: 2026-02-04.

Color Analysis

Rank Color Hex % Family Name
1 33446C 23.2 blue-violet grayish purple
2 4C5E6D 18.3 blue grayish purple
3 2C2A3F 15.7 violet very dark gray
4 847078 14.5 red dusty mauve
5 544539 13.1 orange dark brown
6 5A6B7F 6.5 blue-violet grayish purple
7 6E6558 5.0 yellow-orange dimgray
8 F1D672 1.4 yellow-orange khaki
9 CEAC47 1.3 yellow-orange peru
10 B9B2A7 1.1 yellow-orange steel gray
11 F8EDA0 0.3 yellow palegoldenrod [Accent]
12 E7CBC4 0.3 red-orange lightgray [Accent]

Color Families:

Family %
blue-violet 29.7
blue 18.3
violet 15.7
red 14.5
orange 13.1
yellow-orange 8.7
yellow 0.3
red-orange 0.3

Accent Colors:

Hex Family Name Chroma
F8EDA0 yellow palegoldenrod 39.6
E7CBC4 red-orange lightgray 11.4

Texture Analysis

Metric Value
Global Roughness 0.124
Mean Local Roughness 0.006
Roughness Uniformity 0.016
Edge Density 0.013
Mean Gradient Magnitude 0.045
Gradient Variance 0.017
Gradient Smoothness 0.0
Directional Coherence 0.259
Pattern Complexity 0.102
Pattern Repetition 1.0
Detail Frequency Ratio 0.676
Spatial Variation 0.097
Texture Consistency 0.133

Brightness & Contrast Analysis

Metric Value
Mean Brightness 0.335
Brightness Variance 0.124
Brightness Uniformity 0.629
Brightness Skewness 1.367
Brightness Entropy 6.48
Rms Contrast 0.124
Michelson Contrast 1.0
Weber Contrast 0.605
Mean Local Contrast 0.006
Contrast Uniformity 0.0
Dynamic Range 0.984
Effective Dynamic Range 0.337
Shadow Percentage 52.244
Midtone Percentage 44.768
Highlight Percentage 2.989
Shadow Clipping 0.0
Highlight Clipping 0.0
Tonal Balance 0.0
Fine Contrast 0.004
Medium Contrast 0.008
Coarse Contrast None
Multiscale Contrast Ratio 1.0
Edge Contrast 0.045
Contrast Clustering 0.867

Spatial Distribution Analysis

Metric Value
Spatial Coherence 0.825
Color Clustering 0.444
Color Transition Smoothness 0.874
Transition Uniformity 0.893
Sharp Transition Ratio 0.1
Transition Directionality 0.275
Mean Saturation 0.343
Saturation Variance 0.019
Low Saturation Ratio 0.412
Medium Saturation Ratio 0.584
High Saturation Ratio 0.004
Saturation Clustering 1.0
Hue Concentration 0.485
Complementary Balance 0.198
Analogous Dominance 0.7
Temperature Bias -0.259

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). Ab minor - Research on Harmony - Variation 4 — Catalog raisonné. https://arnaudquercy.art/en/catalogue-raisonne/AQC0691.html

[2] Quercy, A. (2025). Untitled - Gallery. https://artquamanima.com/en/artworks/2024/01/ab-minor-research-on-harmony-variation-4_7oy.html

[3] Quercy, A. (2025). Computational Image Analysis Standard - MMIDS-CMP-2025 https://multimodal.institute/en/publications/2025/10/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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