AQC1084 | NAN-COL000647

Nanopublication — Computational Image Analysis - AQC1084

Watercolor Study in D Major No. 3

Claim 1: Computational Image Analysis - AQC1084

Analysis record: Watercolor Study in D Major No. 3 (AQC1084) [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: 1714x2285 pixels. Analysis date: 2026-07-13.

A) Color Analysis

Rank Color Hex % Family Name
1 B8AD98 18.8 yellow-orange steel gray
2 B39C87 15.4 orange rosybrown
3 C1B8A4 14.3 yellow-orange steel gray
4 557B70 12.7 green dimgray
5 416A60 11.8 green darkslategray
6 A9907A 10.3 orange gray
7 A84E05 10.3 orange russet
8 987C5B 4.0 orange grey
9 120C09 1.4 black black
10 5A4F34 1.1 yellow-orange dark brown
11 4D1C11 0.3 red-orange very dark red [Accent]
12 2E3A30 0.3 yellow-green darkslategray [Accent]

Color Families:

Family %
orange 40.0
yellow-orange 34.2
green 24.5
black 1.4
red-orange 0.3
yellow-green 0.3

Accent Colors:

Hex Family Name Chroma
4D1C11 red-orange very dark red 29.1
2E3A30 yellow-green darkslategray 9.4

B) Texture Analysis

Metric Value
Global Roughness 0.15
Mean Local Roughness 0.019
Roughness Uniformity 0.013
Edge Density 0.036
Mean Gradient Magnitude 0.142
Gradient Variance 0.028
Gradient Smoothness 0.0
Directional Coherence 0.003
Pattern Complexity 0.142
Pattern Repetition 1.0
Detail Frequency Ratio 0.631
Spatial Variation 0.115
Texture Consistency 0.419

C) Brightness & Contrast Analysis

Metric Value
Mean Brightness 0.552
Brightness Variance 0.15
Brightness Uniformity 0.729
Brightness Skewness -0.676
Brightness Entropy 6.784
Rms Contrast 0.15
Michelson Contrast 1.0
Weber Contrast 0.497
Mean Local Contrast 0.02
Contrast Uniformity 0.248
Dynamic Range 0.906
Effective Dynamic Range 0.396
Shadow Percentage 4.419
Midtone Percentage 64.305
Highlight Percentage 31.276
Shadow Clipping 0.01
Highlight Clipping 0.0
Tonal Balance 0.0
Fine Contrast 0.011
Medium Contrast 0.024
Coarse Contrast 0.033
Multiscale Contrast Ratio 0.34
Edge Contrast 0.142
Contrast Clustering 0.581

D) Spatial Distribution Analysis

Metric Value
Spatial Coherence 0.775
Color Clustering 0.58
Color Transition Smoothness 0.638
Transition Uniformity 0.797
Sharp Transition Ratio 0.1
Transition Directionality 0.003
Mean Saturation 0.334
Saturation Variance 0.058
Low Saturation Ratio 0.64
Medium Saturation Ratio 0.25
High Saturation Ratio 0.11
Saturation Clustering 0.999
Hue Concentration 0.444
Complementary Balance 0.005
Analogous Dominance 0.633
Temperature Bias 0.267

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 D Major No. 3 - Catalogue Raisonné. https://arnaudquercy.art/en/catalogue-raisonne/AQC1084.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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