AQC1052 | NAN-COL000615

Nanopublication — Computational Image Analysis - AQC1052

Watercolor Study in A Major No. 1

Claim 1: Computational Image Analysis - AQC1052

Analysis record: Watercolor Study in A Major No. 1 (AQC1052) [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: 1639x2185 pixels. Analysis date: 2026-07-13.

A) Color Analysis

Rank Color Hex % Family Name
1 595458 19.7 gray dusty mauve
2 49464B 18.9 gray dusty mauve
3 BEA25D 16.8 yellow-orange ochre
4 39342E 15.8 yellow-orange darkslategray
5 AF9153 7.8 yellow-orange peru
6 7B6F5D 7.3 yellow-orange dimgray
7 1F1C17 6.2 gray very dark gray
8 294049 3.6 blue darkslategrey
9 7A5F0A 3.5 yellow-orange russet
10 AAACB1 0.3 gray steel gray

Color Families:

Family %
yellow-orange 51.2
gray 45.2
blue 3.6

B) Texture Analysis

Metric Value
Global Roughness 0.166
Mean Local Roughness 0.012
Roughness Uniformity 0.006
Edge Density 0.008
Mean Gradient Magnitude 0.094
Gradient Variance 0.01
Gradient Smoothness 0.0
Directional Coherence 0.01
Pattern Complexity 0.128
Pattern Repetition 1.0
Detail Frequency Ratio 0.598
Spatial Variation 0.128
Texture Consistency 0.488

C) Brightness & Contrast Analysis

Metric Value
Mean Brightness 0.369
Brightness Variance 0.166
Brightness Uniformity 0.551
Brightness Skewness 0.495
Brightness Entropy 6.951
Rms Contrast 0.166
Michelson Contrast 1.0
Weber Contrast 0.704
Mean Local Contrast 0.012
Contrast Uniformity 0.394
Dynamic Range 0.769
Effective Dynamic Range 0.51
Shadow Percentage 53.243
Midtone Percentage 44.607
Highlight Percentage 2.15
Shadow Clipping 0.0
Highlight Clipping 0.0
Tonal Balance 0.0
Fine Contrast 0.008
Medium Contrast 0.015
Coarse Contrast 0.023
Multiscale Contrast Ratio 0.336
Edge Contrast 0.094
Contrast Clustering 0.512

D) Spatial Distribution Analysis

Metric Value
Spatial Coherence 0.786
Color Clustering 0.69
Color Transition Smoothness 0.734
Transition Uniformity 0.921
Sharp Transition Ratio 0.1
Transition Directionality 0.011
Mean Saturation 0.28
Saturation Variance 0.046
Low Saturation Ratio 0.657
Medium Saturation Ratio 0.305
High Saturation Ratio 0.038
Saturation Clustering 1.0
Hue Concentration 0.844
Complementary Balance 0.026
Analogous Dominance 0.92
Temperature Bias 0.843

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 A Major No. 1 - Catalogue Raisonné. https://arnaudquercy.art/en/catalogue-raisonne/AQC1052.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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