AQC1080 | NAN-COL000643

Nanopublication — Computational Image Analysis - AQC1080

Watercolor Study in Bb Minor No. 1

Claim 1: Computational Image Analysis - AQC1080

K-means clustering (10 colors) performed on artwork Watercolor Study in Bb Minor No. 1 (AQC1080) [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: 1798x2578 pixels. Analysis date: 2026-07-13.

A) Color Analysis

Rank Color Hex % Family Name
1 82A88C 16.9 yellow-green darkseagreen
2 739A80 14.7 yellow-green gray
3 608573 9.7 yellow-green dimgray
4 97B49A 9.6 yellow-green steel gray
5 070A09 9.5 black black
6 507062 9.2 green dimgrey
7 6B454B 8.7 red dark brown
8 5B343C 8.2 red darkslategray
9 1C342C 6.9 green darkslategrey
10 45574D 6.7 yellow-green dark brown
11 3B062C 0.3 red-violet very dark purple [Accent]
12 391A1E 0.3 red-orange very dark red [Accent]

Color Families:

Family %
yellow-green 57.5
red 16.9
green 16.1
black 9.5
red-violet 0.3
red-orange 0.3

Accent Colors:

Hex Family Name Chroma
3B062C red-violet very dark purple 32.0
391A1E red-orange very dark red 15.8

B) Texture Analysis

Metric Value
Global Roughness 0.192
Mean Local Roughness 0.019
Roughness Uniformity 0.015
Edge Density 0.057
Mean Gradient Magnitude 0.153
Gradient Variance 0.039
Gradient Smoothness 0.0
Directional Coherence 0.008
Pattern Complexity 0.132
Pattern Repetition 1.0
Detail Frequency Ratio 0.61
Spatial Variation 0.118
Texture Consistency 0.629

C) Brightness & Contrast Analysis

Metric Value
Mean Brightness 0.414
Brightness Variance 0.192
Brightness Uniformity 0.537
Brightness Skewness -0.515
Brightness Entropy 7.281
Rms Contrast 0.192
Michelson Contrast 1.0
Weber Contrast 0.814
Mean Local Contrast 0.021
Contrast Uniformity 0.154
Dynamic Range 0.835
Effective Dynamic Range 0.624
Shadow Percentage 34.801
Midtone Percentage 61.42
Highlight Percentage 3.779
Shadow Clipping 0.087
Highlight Clipping 0.0
Tonal Balance 0.058
Fine Contrast 0.01
Medium Contrast 0.025
Coarse Contrast 0.039
Multiscale Contrast Ratio 0.26
Edge Contrast 0.153
Contrast Clustering 0.371

D) Spatial Distribution Analysis

Metric Value
Spatial Coherence 0.731
Color Clustering 0.901
Color Transition Smoothness 0.592
Transition Uniformity 0.696
Sharp Transition Ratio 0.1
Transition Directionality 0.008
Mean Saturation 0.294
Saturation Variance 0.022
Low Saturation Ratio 0.627
Medium Saturation Ratio 0.349
High Saturation Ratio 0.024
Saturation Clustering 0.996
Hue Concentration 0.543
Complementary Balance 0.088
Analogous Dominance 0.758
Temperature Bias -0.521

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 Bb Minor No. 1 - Catalogue Raisonné. https://arnaudquercy.art/en/catalogue-raisonne/AQC1080.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

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