AQC1053 | NAN-COL000616

Nanopublication — Computational Image Analysis - AQC1053

Watercolor Study in F Minor No. 2

Claim 1: Computational Image Analysis - AQC1053

Analysis record: Watercolor Study in F Minor No. 2 (AQC1053) [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: 1564x2085 pixels. Analysis date: 2026-07-13.

A) Color Analysis

Rank Color Hex % Family Name
1 798884 21.6 green gray
2 707E7B 18.0 green grey
3 535B60 15.2 gray dimgray
4 636569 8.9 gray grayish purple
5 474E54 7.6 blue-violet grayish purple
6 1D0916 7.0 red-violet very dark gray
7 4F1E07 7.0 orange very dark orange
8 070408 6.3 black black
9 301321 5.5 red very dark red
10 091328 2.9 blue-violet very dark indigo
11 A29A86 0.3 yellow-orange rosybrown [Accent]
12 A5A08E 0.3 yellow rosybrown [Accent]

Color Families:

Family %
green 39.6
gray 24.1
blue-violet 10.5
red-violet 7.0
orange 7.0
black 6.3
red 5.5
yellow-orange 0.3
yellow 0.3

Accent Colors:

Hex Family Name Chroma
A29A86 yellow-orange rosybrown 12.0
A5A08E yellow rosybrown 10.0

B) Texture Analysis

Metric Value
Global Roughness 0.172
Mean Local Roughness 0.016
Roughness Uniformity 0.011
Edge Density 0.017
Mean Gradient Magnitude 0.117
Gradient Variance 0.02
Gradient Smoothness 0.0
Directional Coherence 0.01
Pattern Complexity 0.131
Pattern Repetition 1.0
Detail Frequency Ratio 0.616
Spatial Variation 0.12
Texture Consistency 0.62

C) Brightness & Contrast Analysis

Metric Value
Mean Brightness 0.336
Brightness Variance 0.172
Brightness Uniformity 0.489
Brightness Skewness -0.588
Brightness Entropy 6.747
Rms Contrast 0.172
Michelson Contrast 1.0
Weber Contrast 0.886
Mean Local Contrast 0.016
Contrast Uniformity 0.275
Dynamic Range 0.722
Effective Dynamic Range 0.498
Shadow Percentage 37.516
Midtone Percentage 62.481
Highlight Percentage 0.003
Shadow Clipping 0.154
Highlight Clipping 0.0
Tonal Balance 0.0
Fine Contrast 0.01
Medium Contrast 0.02
Coarse Contrast 0.028
Multiscale Contrast Ratio 0.352
Edge Contrast 0.117
Contrast Clustering 0.38

D) Spatial Distribution Analysis

Metric Value
Spatial Coherence 0.743
Color Clustering 0.89
Color Transition Smoothness 0.681
Transition Uniformity 0.841
Sharp Transition Ratio 0.1
Transition Directionality 0.011
Mean Saturation 0.295
Saturation Variance 0.087
Low Saturation Ratio 0.735
Medium Saturation Ratio 0.097
High Saturation Ratio 0.169
Saturation Clustering 0.996
Hue Concentration 0.519
Complementary Balance 0.092
Analogous Dominance 0.601
Temperature Bias 0.446

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 Minor No. 2 - Catalogue Raisonné. https://arnaudquercy.art/en/catalogue-raisonne/AQC1053.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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