AQC0656 | NAN-COL000290

Nanopublication — Computational Image Analysis - AQC0656

Ab minor - Research on Harmony - Variation 3

Claim 1: Computational Image Analysis - AQC0656

The artwork Ab minor - Research on Harmony - Variation 3 (AQC0656) [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: 2565x3420 pixels. Analysis date: 2025-10-03.

A) Color Analysis

Rank Color Hex % Family Name
1 3D3733 14.7 gray darkslategray
2 7F8381 13.1 gray gray
3 6D6665 12.9 gray dimgray
4 565048 11.9 yellow-orange dark brown
5 9B9E97 11.2 gray steel gray
6 C3BCAA 9.5 yellow silver
7 251E11 7.6 yellow-orange very dark gray
8 D6D942 7.0 yellow ochre
9 EADDB7 6.5 yellow wheat
10 BCC020 5.5 yellow goldenrod
11 753536 0.3 red-orange russet [Accent]
12 A76C75 0.3 red gray [Accent]

Color Families:

Family %
gray 52.0
yellow 28.5
yellow-orange 19.5
red-orange 0.3
red 0.3

Accent Colors:

Hex Family Name Chroma
753536 red-orange russet 30.9
A76C75 red gray 25.5

B) Texture Analysis

Metric Value
Global Roughness 0.227
Mean Local Roughness 0.03
Roughness Uniformity 0.022
Edge Density 0.186
Mean Gradient Magnitude 0.239
Gradient Variance 0.062
Gradient Smoothness 0.0
Directional Coherence 0.008
Pattern Complexity 0.12
Pattern Repetition 1.0
Detail Frequency Ratio 0.634
Spatial Variation 0.122
Texture Consistency 0.706

C) Brightness & Contrast Analysis

Metric Value
Mean Brightness 0.488
Brightness Variance 0.227
Brightness Uniformity 0.536
Brightness Skewness 0.056
Brightness Entropy 7.719
Rms Contrast 0.227
Michelson Contrast 1.0
Weber Contrast 0.752
Mean Local Contrast 0.032
Contrast Uniformity 0.286
Dynamic Range 1.0
Effective Dynamic Range 0.71
Shadow Percentage 29.84
Midtone Percentage 42.486
Highlight Percentage 27.674
Shadow Clipping 0.005
Highlight Clipping 0.003
Tonal Balance 0.453
Fine Contrast 0.017
Medium Contrast 0.04
Coarse Contrast 0.057
Multiscale Contrast Ratio 0.297
Edge Contrast 0.239
Contrast Clustering 0.294

D) Spatial Distribution Analysis

Metric Value
Spatial Coherence 0.683
Color Clustering 0.748
Color Transition Smoothness 0.379
Transition Uniformity 0.579
Sharp Transition Ratio 0.1
Transition Directionality 0.011
Mean Saturation 0.263
Saturation Variance 0.059
Low Saturation Ratio 0.771
Medium Saturation Ratio 0.124
High Saturation Ratio 0.106
Saturation Clustering 0.999
Hue Concentration 0.825
Complementary Balance 0.048
Analogous Dominance 0.929
Temperature Bias 0.529

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). Ab minor - Research on Harmony - Variation 3 - Catalogue Raisonné. https://arnaudquercy.art/en/catalogue-raisonne/AQC0656.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

Checksum (SHA-256)

c5939f98688f6940c943a189cc305bb8daa7c82ccbb1c787286eb0222ec07b27

This page in other formats

PDF · Markdown · Français