AQC0605

Nanopublication — Computational Image Analysis - AQC0605

Claim 1: Computational Image Analysis - AQC0605

The artwork G minor - Research [1] on Harmony (AQC0605) [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]: 2678x3570 pixels. Analysis date: 2026-02-04.

Color Analysis

Rank Color Hex % Family Name
1 C8380D 25.1 red-orange firebrick
2 DA4A12 20.9 orange chocolate
3 A73317 14.2 red-orange brown
4 BD4731 11.8 red-orange burnt sienna
5 E5512B 11.3 red-orange tomato
6 DE6653 7.0 red-orange indianred
7 3D152E 4.1 red-violet very dark purple
8 F28D7B 3.3 red-orange darksalmon
9 643354 1.3 red-violet dusty mauve
10 E6D4CA 1.0 orange lightgray
11 1F050D 0.3 red very dark gray [Accent]
12 5A417B 0.3 violet dusty mauve [Accent]

Color Families:

Family %
red-orange 72.7
orange 21.9
red-violet 5.4
red 0.3
violet 0.3

Accent Colors:

Hex Family Name Chroma
1F050D red very dark gray 11.0
5A417B violet dusty mauve 37.6

Texture Analysis

Metric Value
Global Roughness 0.111
Mean Local Roughness 0.032
Roughness Uniformity 0.036
Edge Density 0.13
Mean Gradient Magnitude 0.236
Gradient Variance 0.101
Gradient Smoothness 0.0
Directional Coherence 0.027
Pattern Complexity 0.122
Pattern Repetition 1.0
Detail Frequency Ratio 0.695
Spatial Variation 0.041
Texture Consistency 0.508

Brightness & Contrast Analysis

Metric Value
Mean Brightness 0.411
Brightness Variance 0.111
Brightness Uniformity 0.731
Brightness Skewness 0.685
Brightness Entropy 6.612
Rms Contrast 0.111
Michelson Contrast 1.0
Weber Contrast 0.406
Mean Local Contrast 0.033
Contrast Uniformity 0.0
Dynamic Range 1.0
Effective Dynamic Range 0.357
Shadow Percentage 15.289
Midtone Percentage 81.619
Highlight Percentage 3.092
Shadow Clipping 0.001
Highlight Clipping 0.002
Tonal Balance 0.0
Fine Contrast 0.019
Medium Contrast 0.041
Coarse Contrast 0.054
Multiscale Contrast Ratio 0.356
Edge Contrast 0.236
Contrast Clustering 0.492

Spatial Distribution Analysis

Metric Value
Spatial Coherence 0.721
Color Clustering 0.353
Color Transition Smoothness 0.447
Transition Uniformity 0.427
Sharp Transition Ratio 0.1
Transition Directionality 0.037
Mean Saturation 0.822
Saturation Variance 0.024
Low Saturation Ratio 0.01
Medium Saturation Ratio 0.171
High Saturation Ratio 0.819
Saturation Clustering 0.998
Hue Concentration 0.976
Complementary Balance 0.0
Analogous Dominance 0.981
Temperature Bias 0.993

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). G minor - Research on Harmony — Catalog raisonné. https://arnaudquercy.art/en/catalogue-raisonne/AQC0605.html

[2] Quercy, A. (2025). Untitled - Gallery. https://artquamanima.com/en/artworks/2024/01/g-minor-research-on-harmony_6ri.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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