AQC1124

Nanopublication — Computational Image Analysis - AQC1124

D Minor 7 - Research on Harmony - Variations 8

Claim 1: Computational Image Analysis - AQC1124

K-means clustering (10 colors) performed on artwork D Minor 7 - Research on Harmony - Variations 8 (AQC1124) [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 B8AB99 25.6 yellow-orange steel gray
2 AB9C8A 23.5 yellow-orange rosybrown
3 9F8D78 13.2 yellow-orange gray
4 972709 10.4 red-orange russet
5 564740 6.9 orange dark brown
6 776C5E 5.8 yellow-orange dimgray
7 110A06 4.4 black black
8 E0DACE 3.8 yellow-orange gainsboro
9 A05F38 3.8 orange burnt sienna
10 3D2825 2.8 red-orange very dark gray

Color Families:

Family %
yellow-orange 71.8
red-orange 13.2
orange 10.6
black 4.4

B) Texture Analysis

Metric Value
Global Roughness 0.195
Mean Local Roughness 0.018
Roughness Uniformity 0.01
Edge Density 0.041
Mean Gradient Magnitude 0.121
Gradient Variance 0.017
Gradient Smoothness 0.0
Directional Coherence 0.008
Pattern Complexity 0.137
Pattern Repetition 1.0
Detail Frequency Ratio 0.634
Spatial Variation 0.133
Texture Consistency 0.551

C) Brightness & Contrast Analysis

Metric Value
Mean Brightness 0.525
Brightness Variance 0.195
Brightness Uniformity 0.628
Brightness Skewness -0.79
Brightness Entropy 7.076
Rms Contrast 0.195
Michelson Contrast 1.0
Weber Contrast 0.644
Mean Local Contrast 0.018
Contrast Uniformity 0.357
Dynamic Range 0.996
Effective Dynamic Range 0.565
Shadow Percentage 22.462
Midtone Percentage 54.066
Highlight Percentage 23.473
Shadow Clipping 0.0
Highlight Clipping 0.0
Tonal Balance 0.0
Fine Contrast 0.013
Medium Contrast 0.022
Coarse Contrast 0.028
Multiscale Contrast Ratio 0.474
Edge Contrast 0.121
Contrast Clustering 0.449

D) Spatial Distribution Analysis

Metric Value
Spatial Coherence 0.743
Color Clustering 0.699
Color Transition Smoothness 0.694
Transition Uniformity 0.883
Sharp Transition Ratio 0.1
Transition Directionality 0.009
Mean Saturation 0.311
Saturation Variance 0.067
Low Saturation Ratio 0.712
Medium Saturation Ratio 0.153
High Saturation Ratio 0.134
Saturation Clustering 0.999
Hue Concentration 0.977
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. (2026). D Minor 7 - Research on Harmony - Variations 8 - Catalogue Raisonné. https://arnaudquercy.art/en/catalogue-raisonne/AQC1124.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

Checksum (SHA-256)

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