AQC1142

Nanopublication — Computational Image Analysis - AQC1142

F Minor - Research on Harmony - Variations 24

Claim 1: Computational Image Analysis - AQC1142

K-means clustering (10 colors) performed on artwork F Minor - Research on Harmony - Variations 24 (AQC1142) [1] by Arnaud Quercy [2] on 2026-10-02, according to IDS-CMP-2025 [3]. Documentation includes: color families, texture roughness, brightness distribution, spatial coherence.

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: 2026-10-02.

A) Color Analysis

Rank Color Hex % Family Name
1 D1C4B1 39.1 yellow-orange silver
2 BAAB97 15.2 yellow-orange steel gray
3 9F9789 10.2 yellow-orange rosybrown
4 60626D 7.3 violet dusty mauve
5 130E0C 7.1 black black
6 1A2236 6.5 violet very dark gray
7 7F778B 6.1 violet dusty mauve
8 8E1C09 3.9 red-orange darkred
9 8B6E58 3.4 orange dimgray
10 AC4638 1.1 red-orange burnt sienna
11 58353B 0.3 red darkslategray [Accent]

Color Families:

Family %
yellow-orange 64.6
violet 19.9
black 7.1
red-orange 5.0
orange 3.4
red 0.3

Accent Colors:

Hex Family Name Chroma
58353B red darkslategray 17.3

B) Texture Analysis

Metric Value
Global Roughness 0.242
Mean Local Roughness 0.013
Roughness Uniformity 0.006
Edge Density 0.01
Mean Gradient Magnitude 0.083
Gradient Variance 0.008
Gradient Smoothness 0.0
Directional Coherence 0.024
Pattern Complexity 0.136
Pattern Repetition 1.0
Detail Frequency Ratio 0.618
Spatial Variation 0.169
Texture Consistency 0.567

C) Brightness & Contrast Analysis

Metric Value
Mean Brightness 0.57
Brightness Variance 0.242
Brightness Uniformity 0.575
Brightness Skewness -0.925
Brightness Entropy 7.051
Rms Contrast 0.242
Michelson Contrast 1.0
Weber Contrast 0.837
Mean Local Contrast 0.012
Contrast Uniformity 0.41
Dynamic Range 0.984
Effective Dynamic Range 0.725
Shadow Percentage 18.176
Midtone Percentage 31.823
Highlight Percentage 50.001
Shadow Clipping 0.007
Highlight Clipping 0.0
Tonal Balance 0.0
Fine Contrast 0.01
Medium Contrast 0.015
Coarse Contrast 0.019
Multiscale Contrast Ratio 0.518
Edge Contrast 0.083
Contrast Clustering 0.433

D) Spatial Distribution Analysis

Metric Value
Spatial Coherence 0.762
Color Clustering 0.823
Color Transition Smoothness 0.785
Transition Uniformity 0.939
Sharp Transition Ratio 0.1
Transition Directionality 0.025
Mean Saturation 0.246
Saturation Variance 0.043
Low Saturation Ratio 0.808
Medium Saturation Ratio 0.128
High Saturation Ratio 0.063
Saturation Clustering 0.999
Hue Concentration 0.488
Complementary Balance 0.034
Analogous Dominance 0.733
Temperature Bias 0.479

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. (2026). F Minor - Research on Harmony - Variations 24 - Catalogue Raisonné. https://arnaudquercy.art/en/catalogue-raisonne/AQC1142.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/2025/11/ids-cmp-2025-computational-image-analysis-standard-5dq9.html

Where this work lives

Epistemic profile

Claim typecomputational analysis
Voicethird person
Epistemic statusempirical measurement
Methodologycomputational analysis
Certaintyhigh

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