AQC1086 | NAN-COL000649

Nanopublication — Computational Image Analysis - AQC1086

Watercolor Study in Eb Major No. 2

Claim 1: Computational Image Analysis - AQC1086

The artwork Watercolor Study in Eb Major No. 2 (AQC1086) [1] by Arnaud Quercy [2] underwent comprehensive computational analysis [3] on 2026-07-13. 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: 1596x2143 pixels. Analysis date: 2026-07-13.

A) Color Analysis

Rank Color Hex % Family Name
1 AA89AD 22.5 red-violet steel gray
2 9C7DA2 14.1 red-violet dusty mauve
3 807894 12.5 violet dusty mauve
4 53474C 11.3 red dusty mauve
5 B797B8 10.3 red-violet steel gray
6 726984 9.2 violet dusty mauve
7 655360 7.7 red-violet dusty mauve
8 42383A 7.2 red darkslategray
9 110710 2.8 red-violet black
10 4E1044 2.5 red-violet very dark purple
11 56612D 0.3 yellow-green dark brown [Accent]

Color Families:

Family %
red-violet 59.9
violet 21.6
red 18.5
yellow-green 0.3

Accent Colors:

Hex Family Name Chroma
56612D yellow-green dark brown 30.9

B) Texture Analysis

Metric Value
Global Roughness 0.156
Mean Local Roughness 0.022
Roughness Uniformity 0.015
Edge Density 0.079
Mean Gradient Magnitude 0.169
Gradient Variance 0.038
Gradient Smoothness 0.0
Directional Coherence 0.003
Pattern Complexity 0.135
Pattern Repetition 1.0
Detail Frequency Ratio 0.625
Spatial Variation 0.09
Texture Consistency 0.702

C) Brightness & Contrast Analysis

Metric Value
Mean Brightness 0.462
Brightness Variance 0.156
Brightness Uniformity 0.663
Brightness Skewness -0.787
Brightness Entropy 7.013
Rms Contrast 0.156
Michelson Contrast 1.0
Weber Contrast 0.604
Mean Local Contrast 0.023
Contrast Uniformity 0.261
Dynamic Range 1.0
Effective Dynamic Range 0.455
Shadow Percentage 24.376
Midtone Percentage 73.712
Highlight Percentage 1.912
Shadow Clipping 0.014
Highlight Clipping 0.003
Tonal Balance 0.0
Fine Contrast 0.013
Medium Contrast 0.028
Coarse Contrast 0.041
Multiscale Contrast Ratio 0.323
Edge Contrast 0.169
Contrast Clustering 0.298

D) Spatial Distribution Analysis

Metric Value
Spatial Coherence 0.704
Color Clustering 0.89
Color Transition Smoothness 0.554
Transition Uniformity 0.719
Sharp Transition Ratio 0.1
Transition Directionality 0.004
Mean Saturation 0.225
Saturation Variance 0.021
Low Saturation Ratio 0.909
Medium Saturation Ratio 0.056
High Saturation Ratio 0.035
Saturation Clustering 0.998
Hue Concentration 0.915
Complementary Balance 0.014
Analogous Dominance 0.947
Temperature Bias 0.265

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. 2 - Catalogue Raisonné. https://arnaudquercy.art/en/catalogue-raisonne/AQC1086.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)

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