AQC0338

Nanopublication — Computational Image Analysis - AQC0338

Claim 1: Computational Image Analysis - AQC0338

Computational image analysis [3] of artwork God [1]'s vacations (AQC0338) [2] by Arnaud Quercy [2] using k-means clustering method with 10 color extraction parameters. Analysis includes color distribution, texture metrics, brightness/contrast measurements, and spatial pattern characterization. Analysis completed on 2026-02-04.

Context

Analysis performed according to MMIDS-CMP-2025 [3] includes four metric categories: (1) Color distribution via k-means (10 colors), (2) Texture analysis using Haralick features, (3) Brightness and contrast measurements, (4) Spatial pattern characterization. Source image [5]: 2808x3744 pixels. Analysis date: 2026-02-04.

Color Analysis

Rank Color Hex % Family Name
1 0B0606 22.7 black black
2 221714 13.7 red-orange black
3 812D1E 11.3 red-orange russet
4 5A3228 11.0 red-orange russet
5 352929 11.0 red-orange very dark gray
6 8C4A34 10.2 red-orange burnt sienna
7 A95F45 9.2 orange burnt sienna
8 B88162 4.9 orange peru
9 5B5353 4.8 gray dimgray
10 D2C2AD 1.3 yellow-orange silver
11 52677C 0.3 blue-violet grayish purple [Accent]
12 314756 0.3 blue grayish purple [Accent]

Color Families:

Family %
red-orange 57.2
black 22.7
orange 14.0
gray 4.8
yellow-orange 1.3
blue-violet 0.3
blue 0.3

Accent Colors:

Hex Family Name Chroma
52677C blue-violet grayish purple 14.1
314756 blue grayish purple 12.6

Texture Analysis

Metric Value
Global Roughness 0.168
Mean Local Roughness 0.017
Roughness Uniformity 0.008
Edge Density 0.04
Mean Gradient Magnitude 0.124
Gradient Variance 0.011
Gradient Smoothness 0.145
Directional Coherence 0.01
Pattern Complexity 0.129
Pattern Repetition 1.0
Detail Frequency Ratio 0.627
Spatial Variation 0.093
Texture Consistency 0.727

Brightness & Contrast Analysis

Metric Value
Mean Brightness 0.229
Brightness Variance 0.168
Brightness Uniformity 0.266
Brightness Skewness 0.692
Brightness Entropy 7.169
Rms Contrast 0.168
Michelson Contrast 1.0
Weber Contrast 0.94
Mean Local Contrast 0.016
Contrast Uniformity 0.491
Dynamic Range 0.925
Effective Dynamic Range 0.51
Shadow Percentage 74.177
Midtone Percentage 24.439
Highlight Percentage 1.384
Shadow Clipping 0.353
Highlight Clipping 0.0
Tonal Balance 0.0
Fine Contrast 0.01
Medium Contrast 0.02
Coarse Contrast None
Multiscale Contrast Ratio 1.0
Edge Contrast 0.124
Contrast Clustering 0.273

Spatial Distribution Analysis

Metric Value
Spatial Coherence 0.691
Color Clustering 0.636
Color Transition Smoothness 0.659
Transition Uniformity 0.911
Sharp Transition Ratio 0.1
Transition Directionality 0.019
Mean Saturation 0.524
Saturation Variance 0.058
Low Saturation Ratio 0.199
Medium Saturation Ratio 0.537
High Saturation Ratio 0.264
Saturation Clustering 0.995
Hue Concentration 0.799
Complementary Balance 0.066
Analogous Dominance 0.891
Temperature Bias 0.807

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 Multimodal Institute's computational imaging systems.

References

[1] Arnaud Quercy (2022). God's vacations — Catalog raisonné. https://arnaudquercy.art/en/catalogue-raisonne/AQC0338.html

[2] Quercy, A. (2025). Untitled - Gallery. https://artquamanima.com/en/artworks/2022/01/gods-vacations_3vo.html

[3] Quercy, A. (2025). Computational Image Analysis Standard - MMIDS-CMP-2025 https://multimodal.institute/en/publications/2025/11/mmids-cmp-2025-computational-image-analysis-standard-dg1.html

Epistemic profile

Claim typecomputational analysis
Voicethird person
Epistemic statusempirical measurement
Methodologycomputational analysis
Certaintyhigh

Checksum (SHA-256)

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