AQC1066 | NAN-COL000629

Nanopublication — Computational Image Analysis - AQC1066

Watercolor Study in F Major No. 1

Claim 1: Computational Image Analysis - AQC1066

Analysis record: Watercolor Study in F Major No. 1 (AQC1066) [1] by Arnaud Quercy [2], per IDS-CMP-2025 [3]. Method: k-means. Parameters: 10 colors. Metrics: color distribution, texture, brightness, spatial patterns. Completed: 2026-07-13.

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: 1753x1753 pixels. Analysis date: 2026-07-13.

A) Color Analysis

Rank Color Hex % Family Name
1 AE7245 19.6 orange peru
2 8D5226 14.3 orange burnt sienna
3 28160F 11.2 red-orange very dark gray
4 B57D53 10.6 orange indianred
5 834717 9.6 orange russet
6 A06539 9.2 orange burnt sienna
7 38261C 8.9 orange very dark gray
8 522F2E 7.4 red-orange darkslategray
9 080401 5.4 black black
10 694139 3.8 red-orange dark brown
11 4F041F 0.3 red very dark red [Accent]

Color Families:

Family %
orange 72.2
red-orange 22.4
black 5.4
red 0.3

Accent Colors:

Hex Family Name Chroma
4F041F red very dark red 35.4

B) Texture Analysis

Metric Value
Global Roughness 0.162
Mean Local Roughness 0.015
Roughness Uniformity 0.011
Edge Density 0.01
Mean Gradient Magnitude 0.108
Gradient Variance 0.019
Gradient Smoothness 0.0
Directional Coherence 0.016
Pattern Complexity 0.132
Pattern Repetition 1.0
Detail Frequency Ratio 0.615
Spatial Variation 0.119
Texture Consistency 0.411

C) Brightness & Contrast Analysis

Metric Value
Mean Brightness 0.338
Brightness Variance 0.162
Brightness Uniformity 0.52
Brightness Skewness -0.442
Brightness Entropy 6.974
Rms Contrast 0.162
Michelson Contrast 1.0
Weber Contrast 0.806
Mean Local Contrast 0.015
Contrast Uniformity 0.158
Dynamic Range 0.678
Effective Dynamic Range 0.49
Shadow Percentage 40.301
Midtone Percentage 59.699
Highlight Percentage 0.0
Shadow Clipping 0.061
Highlight Clipping 0.0
Tonal Balance 0.0
Fine Contrast 0.009
Medium Contrast 0.018
Coarse Contrast None
Multiscale Contrast Ratio 1.0
Edge Contrast 0.108
Contrast Clustering 0.589

D) Spatial Distribution Analysis

Metric Value
Spatial Coherence 0.767
Color Clustering 0.617
Color Transition Smoothness 0.703
Transition Uniformity 0.853
Sharp Transition Ratio 0.1
Transition Directionality 0.018
Mean Saturation 0.633
Saturation Variance 0.022
Low Saturation Ratio 0.003
Medium Saturation Ratio 0.692
High Saturation Ratio 0.305
Saturation Clustering 0.998
Hue Concentration 0.974
Complementary Balance 0.001
Analogous Dominance 0.978
Temperature Bias 0.979

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 F Major No. 1 - Catalogue Raisonné. https://arnaudquercy.art/en/catalogue-raisonne/AQC1066.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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