AQC1089 | NAN-COL000652

Nanopublication — Computational Image Analysis - AQC1089

Watercolor Study in A Minor No. 3

Claim 1: Computational Image Analysis - AQC1089

Analysis record: Watercolor Study in A Minor No. 3 (AQC1089) [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: 1891x2521 pixels. Analysis date: 2026-07-13.

A) Color Analysis

Rank Color Hex % Family Name
1 9FA8A0 22.8 yellow-green steel gray
2 919B94 19.3 yellow-green lightslategray
3 B4B6AD 14.3 gray steel gray
4 896952 13.2 orange dimgray
5 7B5C46 9.1 orange dimgrey
6 99775F 5.8 orange gray
7 9A6425 5.2 orange burnt sienna
8 8D4F11 4.2 orange russet
9 080604 3.9 black black
10 4F4136 2.0 orange darkslategray
11 988F3A 0.3 yellow olivedrab [Accent]
12 28251F 0.3 yellow-orange very dark gray [Accent]

Color Families:

Family %
yellow-green 42.2
orange 39.6
gray 14.3
black 3.9
yellow 0.3
yellow-orange 0.3

Accent Colors:

Hex Family Name Chroma
988F3A yellow olivedrab 45.7
28251F yellow-orange very dark gray 5.0

B) Texture Analysis

Metric Value
Global Roughness 0.159
Mean Local Roughness 0.02
Roughness Uniformity 0.012
Edge Density 0.04
Mean Gradient Magnitude 0.148
Gradient Variance 0.027
Gradient Smoothness 0.0
Directional Coherence 0.008
Pattern Complexity 0.14
Pattern Repetition 1.0
Detail Frequency Ratio 0.625
Spatial Variation 0.098
Texture Consistency 0.394

C) Brightness & Contrast Analysis

Metric Value
Mean Brightness 0.53
Brightness Variance 0.159
Brightness Uniformity 0.699
Brightness Skewness -1.219
Brightness Entropy 6.814
Rms Contrast 0.159
Michelson Contrast 1.0
Weber Contrast 0.475
Mean Local Contrast 0.02
Contrast Uniformity 0.304
Dynamic Range 0.886
Effective Dynamic Range 0.443
Shadow Percentage 6.806
Midtone Percentage 74.529
Highlight Percentage 18.665
Shadow Clipping 0.146
Highlight Clipping 0.0
Tonal Balance 0.0
Fine Contrast 0.012
Medium Contrast 0.025
Coarse Contrast 0.035
Multiscale Contrast Ratio 0.339
Edge Contrast 0.148
Contrast Clustering 0.606

D) Spatial Distribution Analysis

Metric Value
Spatial Coherence 0.755
Color Clustering 0.738
Color Transition Smoothness 0.622
Transition Uniformity 0.8
Sharp Transition Ratio 0.1
Transition Directionality 0.009
Mean Saturation 0.251
Saturation Variance 0.068
Low Saturation Ratio 0.599
Medium Saturation Ratio 0.302
High Saturation Ratio 0.099
Saturation Clustering 0.998
Hue Concentration 0.981
Complementary Balance 0.002
Analogous Dominance 0.979
Temperature Bias 0.971

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 Minor No. 3 - Catalogue Raisonné. https://arnaudquercy.art/en/catalogue-raisonne/AQC1089.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)

e5f74076d72c1990f2ab450fb5809fa85e8eff7ebe55a6e1cf6258292f549cc1

This page in other formats

PDF · Markdown · Français