AQC0761

Nanopublication — Computational Image Analysis - AQC0761

Claim 1: Computational Image Analysis - AQC0761

Analysis record [3]: F Minor [1] - Research on Harmony - Variation 14 (AQC0761) [2] by Arnaud Quercy [2]. Method: k-means. Parameters: 10 colors. Metrics: color distribution, texture, brightness, spatial patterns. Completed: 2026-02-04.

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 DD7597 24.2 red palevioletred
2 30242C 20.1 red-violet very dark gray
3 583B63 10.2 red-violet dusty mauve
4 6B4F7D 9.6 violet dusty mauve
5 EE90AE 9.2 red hotpink
6 71AEE9 6.8 blue-violet cornflowerblue
7 48A2EF 6.0 blue-violet dodgerblue
8 C94965 5.5 red indianred
9 B23543 5.4 red-orange brown
10 4469BC 3.0 violet steelblue
11 AACEEA 0.3 blue lightblue [Accent]
12 F4BCA6 0.3 orange lightpink [Accent]
13 887F5B 0.3 yellow gray [Accent]

Color Families:

Family %
red 38.9
red-violet 30.3
blue-violet 12.8
violet 12.6
red-orange 5.4
blue 0.3
orange 0.3
yellow 0.3

Accent Colors:

Hex Family Name Chroma
AACEEA blue lightblue 19.0
F4BCA6 orange lightpink 25.5
887F5B yellow gray 21.2

Texture Analysis

Metric Value
Global Roughness 0.19
Mean Local Roughness 0.011
Roughness Uniformity 0.011
Edge Density 0.041
Mean Gradient Magnitude 0.113
Gradient Variance 0.021
Gradient Smoothness 0.0
Directional Coherence 0.03
Pattern Complexity 0.116
Pattern Repetition 1.0
Detail Frequency Ratio 0.58
Spatial Variation 0.148
Texture Consistency 0.486

Brightness & Contrast Analysis

Metric Value
Mean Brightness 0.44
Brightness Variance 0.19
Brightness Uniformity 0.568
Brightness Skewness -0.227
Brightness Entropy 7.144
Rms Contrast 0.19
Michelson Contrast 1.0
Weber Contrast 0.752
Mean Local Contrast 0.014
Contrast Uniformity 0.0
Dynamic Range 0.996
Effective Dynamic Range 0.541
Shadow Percentage 32.572
Midtone Percentage 60.61
Highlight Percentage 6.818
Shadow Clipping 0.0
Highlight Clipping 0.0
Tonal Balance 0.0
Fine Contrast 0.005
Medium Contrast 0.017
Coarse Contrast 0.032
Multiscale Contrast Ratio 0.162
Edge Contrast 0.113
Contrast Clustering 0.514

Spatial Distribution Analysis

Metric Value
Spatial Coherence 0.759
Color Clustering 0.571
Color Transition Smoothness 0.705
Transition Uniformity 0.858
Sharp Transition Ratio 0.1
Transition Directionality 0.043
Mean Saturation 0.453
Saturation Variance 0.025
Low Saturation Ratio 0.198
Medium Saturation Ratio 0.725
High Saturation Ratio 0.077
Saturation Clustering 1.0
Hue Concentration 0.673
Complementary Balance 0.007
Analogous Dominance 0.674
Temperature Bias 0.461

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). F Minor - Research on Harmony - Variation 14 — Catalog raisonné. https://arnaudquercy.art/en/catalogue-raisonne/AQC0761.html

[2] Quercy, A. (2025). Untitled - Gallery. https://artquamanima.com/en/artworks/2024/01/f-minor-research-on-harmony-variation-14_8g6.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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