AQC0738 | NAN-COL000216

Nanopublication — Computational Image Analysis - AQC0738

F Minor - Research on Harmony - Variation 12

Claim 1: Computational Image Analysis - AQC0738

The artwork F Minor - Research on Harmony - Variation 12 (AQC0738) [1] by Arnaud Quercy [2] underwent comprehensive computational analysis [3] on 2025-10-03. Method: k-means clustering with 10 colors extracted. Metrics documented: color distribution, texture analysis, brightness/contrast, spatial patterns.

Context

Analysis performed according to IDS-CMP-2025 [3] includes four metric categories: (a) Color distribution via k-means (10 colors), (b) Texture analysis using Haralick features, (c) Brightness and contrast measurements, (d) Spatial pattern characterization. Source image: 3024x4032 pixels. Analysis date: 2025-10-03.

A) Color Analysis

Rank Color Hex % Family Name
1 9DA1A5 23.4 gray steel gray
2 F14745 16.7 red-orange tomato
3 4C1E0C 14.8 orange very dark orange
4 5A3B48 11.9 red dusty mauve
5 ACB1B7 8.8 gray steel gray
6 E2332E 6.3 red-orange crimson
7 6B4D57 5.9 red dusty mauve
8 8B3840 5.7 red-orange brown
9 9F4E58 5.1 red-orange burnt sienna
10 D8916F 1.4 orange darksalmon
11 BDA789 0.3 yellow-orange rosybrown [Accent]

Color Families:

Family %
red-orange 33.8
gray 32.2
red 17.8
orange 16.2
yellow-orange 0.3

Accent Colors:

Hex Family Name Chroma
BDA789 yellow-orange rosybrown 18.2

B) Texture Analysis

Metric Value
Global Roughness 0.178
Mean Local Roughness 0.015
Roughness Uniformity 0.016
Edge Density 0.065
Mean Gradient Magnitude 0.12
Gradient Variance 0.026
Gradient Smoothness 0.0
Directional Coherence 0.035
Pattern Complexity 0.12
Pattern Repetition 1.0
Detail Frequency Ratio 0.644
Spatial Variation 0.146
Texture Consistency 0.482

C) Brightness & Contrast Analysis

Metric Value
Mean Brightness 0.44
Brightness Variance 0.178
Brightness Uniformity 0.595
Brightness Skewness -0.106
Brightness Entropy 7.094
Rms Contrast 0.178
Michelson Contrast 1.0
Weber Contrast 0.734
Mean Local Contrast 0.016
Contrast Uniformity 0.0
Dynamic Range 1.0
Effective Dynamic Range 0.541
Shadow Percentage 32.398
Midtone Percentage 58.725
Highlight Percentage 8.877
Shadow Clipping 0.0
Highlight Clipping 0.0
Tonal Balance 0.0
Fine Contrast 0.008
Medium Contrast 0.02
Coarse Contrast None
Multiscale Contrast Ratio 1.0
Edge Contrast 0.12
Contrast Clustering 0.518

D) Spatial Distribution Analysis

Metric Value
Spatial Coherence 0.782
Color Clustering 0.404
Color Transition Smoothness 0.705
Transition Uniformity 0.839
Sharp Transition Ratio 0.1
Transition Directionality 0.051
Mean Saturation 0.438
Saturation Variance 0.098
Low Saturation Ratio 0.376
Medium Saturation Ratio 0.29
High Saturation Ratio 0.333
Saturation Clustering 0.999
Hue Concentration 0.963
Complementary Balance 0.0
Analogous Dominance 0.999
Temperature Bias 1.0

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 Ideamorphic Studies' computational imaging systems.

References

  1. [1] Quercy, A. (2024). F Minor - Research on Harmony - Variation 12 - Catalogue Raisonné. https://arnaudquercy.art/en/catalogue-raisonne/AQC0738.html
  2. [2] Quercy, A. (2025). ORCID https://orcid.org/0009-0000-2662-7790
  3. [3] Quercy, A. (2026). Computational Image Analysis Standard. https://ideamorphism.org/en/measurements/2025/09/ids-cmp-2025-computational-image-analysis-standard-5dq9.html

Epistemic profile

Claim typecomputational analysis
Voicethird person
Epistemic statusempirical measurement
Methodologycomputational analysis
Certaintyhigh

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