AQC1070 | NAN-COL000633

Nanopublication — Computational Image Analysis - AQC1070

Watercolor Study in Eb Major No. 1

Claim 1: Computational Image Analysis - AQC1070

K-means clustering (10 colors) performed on artwork Watercolor Study in Eb Major No. 1 (AQC1070) [1] by Arnaud Quercy [2] on 2026-07-13, 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: 2186x2186 pixels. Analysis date: 2026-07-13.

A) Color Analysis

Rank Color Hex % Family Name
1 9BA8A7 21.2 green steel gray
2 ADB9B8 20.8 gray silver
3 535A5C 16.4 gray dimgray
4 444949 10.0 gray darkslategray
5 666C6D 7.8 gray dimgrey
6 728C99 7.5 blue lightslategray
7 08243C 7.3 blue-violet very dark indigo
8 457486 3.8 blue blue gray
9 0F5872 3.6 blue teal
10 833D32 1.6 red-orange russet
11 1D1217 0.3 red black [Accent]
12 25191F 0.3 red-violet very dark gray [Accent]

Color Families:

Family %
gray 55.0
green 21.2
blue 14.9
blue-violet 7.3
red-orange 1.6
red 0.3
red-violet 0.3

Accent Colors:

Hex Family Name Chroma
1D1217 red black 8.2
25191F red-violet very dark gray 7.3

B) Texture Analysis

Metric Value
Global Roughness 0.192
Mean Local Roughness 0.016
Roughness Uniformity 0.008
Edge Density 0.045
Mean Gradient Magnitude 0.146
Gradient Variance 0.016
Gradient Smoothness 0.134
Directional Coherence 0.003
Pattern Complexity 0.119
Pattern Repetition 1.0
Detail Frequency Ratio 0.608
Spatial Variation 0.155
Texture Consistency 0.549

C) Brightness & Contrast Analysis

Metric Value
Mean Brightness 0.482
Brightness Variance 0.192
Brightness Uniformity 0.602
Brightness Skewness -0.237
Brightness Entropy 7.18
Rms Contrast 0.192
Michelson Contrast 1.0
Weber Contrast 0.648
Mean Local Contrast 0.019
Contrast Uniformity 0.472
Dynamic Range 0.89
Effective Dynamic Range 0.588
Shadow Percentage 25.742
Midtone Percentage 47.91
Highlight Percentage 26.347
Shadow Clipping 0.002
Highlight Clipping 0.0
Tonal Balance 0.0
Fine Contrast 0.009
Medium Contrast 0.022
Coarse Contrast 0.036
Multiscale Contrast Ratio 0.25
Edge Contrast 0.146
Contrast Clustering 0.451

D) Spatial Distribution Analysis

Metric Value
Spatial Coherence 0.774
Color Clustering 0.75
Color Transition Smoothness 0.616
Transition Uniformity 0.889
Sharp Transition Ratio 0.1
Transition Directionality 0.003
Mean Saturation 0.208
Saturation Variance 0.071
Low Saturation Ratio 0.803
Medium Saturation Ratio 0.1
High Saturation Ratio 0.097
Saturation Clustering 1.0
Hue Concentration 0.828
Complementary Balance 0.037
Analogous Dominance 0.914
Temperature Bias -0.824

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). Watercolor Study in Eb Major No. 1 - Catalogue Raisonné. https://arnaudquercy.art/en/catalogue-raisonne/AQC1070.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)

bee9e2f76dbe38cec725f7a21f9ea08ef07bb8bbf4a27d972a2bedc9260fdb47

This page in other formats

PDF · Markdown · Français