AQC0725

Nanopublication — Computational Image Analysis - AQC0725

by Arnaud Quercy · Db Major - Research on Harmony - Variation 8 · 2024

Claim 1: Computational Image Analysis - AQC0725

Analysis record [3]: Db Major [1] - Research on Harmony - Variation 8 (AQC0725) [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 D43832 21.2 red-orange crimson
2 999A9A 16.6 gray steel gray
3 EC534D 14.6 red-orange tomato
4 4D200D 11.4 orange very dark orange
5 929B66 11.1 yellow-green gray
6 5F3023 10.6 red-orange russet
7 AAAE75 5.7 yellow-green ochre
8 804E35 4.6 orange burnt sienna
9 737A76 3.6 gray grey
10 EFA458 0.7 orange sandybrown
11 1E1500 0.3 yellow-orange very dark gray [Accent]

Color Families:

Family %
red-orange 46.3
gray 20.1
yellow-green 16.8
orange 16.7
yellow-orange 0.3

Accent Colors:

Hex Family Name Chroma
1E1500 yellow-orange very dark gray 11.2

Texture Analysis

Metric Value
Global Roughness 0.156
Mean Local Roughness 0.012
Roughness Uniformity 0.012
Edge Density 0.041
Mean Gradient Magnitude 0.107
Gradient Variance 0.018
Gradient Smoothness 0.0
Directional Coherence 0.03
Pattern Complexity 0.121
Pattern Repetition 1.0
Detail Frequency Ratio 0.626
Spatial Variation 0.125
Texture Consistency 0.361

Brightness & Contrast Analysis

Metric Value
Mean Brightness 0.444
Brightness Variance 0.156
Brightness Uniformity 0.648
Brightness Skewness -0.404
Brightness Entropy 7.066
Rms Contrast 0.156
Michelson Contrast 1.0
Weber Contrast 0.688
Mean Local Contrast 0.014
Contrast Uniformity 0.042
Dynamic Range 1.0
Effective Dynamic Range 0.482
Shadow Percentage 24.057
Midtone Percentage 72.708
Highlight Percentage 3.236
Shadow Clipping 0.0
Highlight Clipping 0.0
Tonal Balance 0.0
Fine Contrast 0.006
Medium Contrast 0.017
Coarse Contrast None
Multiscale Contrast Ratio 1.0
Edge Contrast 0.107
Contrast Clustering 0.639

Spatial Distribution Analysis

Metric Value
Spatial Coherence 0.815
Color Clustering 0.509
Color Transition Smoothness 0.73
Transition Uniformity 0.876
Sharp Transition Ratio 0.1
Transition Directionality 0.044
Mean Saturation 0.524
Saturation Variance 0.081
Low Saturation Ratio 0.209
Medium Saturation Ratio 0.418
High Saturation Ratio 0.373
Saturation Clustering 0.999
Hue Concentration 0.896
Complementary Balance 0.0
Analogous Dominance 0.984
Temperature Bias 0.801

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 Major - Research on Harmony - Variation 8 — Catalog raisonné. https://arnaudquercy.art/en/catalogue-raisonne/AQC0725.html

[2] Quercy, A. (2025). Untitled - Gallery. https://artquamanima.com/en/artworks/2024/01/db-major-research-on-harmony-variation-8_826.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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