AQC0683

Nanopublication — Computational Image Analysis - AQC0683

by Arnaud Quercy · Tritone (D, G#) - Reflexions 17 · 2024

Claim 1: Computational Image Analysis - AQC0683

Computational image analysis [3] of artwork Tritone [1] (D, G#) - Reflexions 17 (AQC0683) [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]: 2612x3483 pixels. Analysis date: 2026-02-04.

Color Analysis

Rank Color Hex % Family Name
1 C7B9C8 21.5 red-violet silver
2 C2ADB6 17.7 red steel gray
3 CAC7DB 14.2 violet thistle
4 A48B8D 12.9 red rosybrown
5 B39C9F 11.3 red steel gray
6 93797C 10.5 red gray
7 85615F 4.1 red-orange dimgray
8 6A6678 3.8 violet dusty mauve
9 4F475A 3.0 violet dusty mauve
10 17142C 1.0 violet very dark purple
11 1D3358 0.3 blue-violet grayish purple [Accent]

Color Families:

Family %
red 52.3
violet 22.0
red-violet 21.5
red-orange 4.1
blue-violet 0.3

Accent Colors:

Hex Family Name Chroma
1D3358 blue-violet grayish purple 25.5

Texture Analysis

Metric Value
Global Roughness 0.143
Mean Local Roughness 0.018
Roughness Uniformity 0.018
Edge Density 0.091
Mean Gradient Magnitude 0.151
Gradient Variance 0.036
Gradient Smoothness 0.0
Directional Coherence 0.009
Pattern Complexity 0.125
Pattern Repetition 1.0
Detail Frequency Ratio 0.62
Spatial Variation 0.084
Texture Consistency 0.554

Brightness & Contrast Analysis

Metric Value
Mean Brightness 0.643
Brightness Variance 0.143
Brightness Uniformity 0.778
Brightness Skewness -1.205
Brightness Entropy 6.888
Rms Contrast 0.143
Michelson Contrast 1.0
Weber Contrast 0.43
Mean Local Contrast 0.019
Contrast Uniformity 0.063
Dynamic Range 0.98
Effective Dynamic Range 0.431
Shadow Percentage 3.299
Midtone Percentage 41.12
Highlight Percentage 55.581
Shadow Clipping 0.005
Highlight Clipping 0.0
Tonal Balance 0.0
Fine Contrast 0.01
Medium Contrast 0.024
Coarse Contrast 0.038
Multiscale Contrast Ratio 0.265
Edge Contrast 0.151
Contrast Clustering 0.446

Spatial Distribution Analysis

Metric Value
Spatial Coherence 0.71
Color Clustering 0.837
Color Transition Smoothness 0.615
Transition Uniformity 0.757
Sharp Transition Ratio 0.1
Transition Directionality 0.013
Mean Saturation 0.144
Saturation Variance 0.01
Low Saturation Ratio 0.945
Medium Saturation Ratio 0.047
High Saturation Ratio 0.007
Saturation Clustering 0.999
Hue Concentration 0.734
Complementary Balance 0.0
Analogous Dominance 0.826
Temperature Bias 0.709

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 (2024). Tritone (D, G#) - Reflexions 17 — Catalog raisonné. https://arnaudquercy.art/en/catalogue-raisonne/AQC0683.html

[2] Quercy, A. (2025). Untitled - Gallery. https://artquamanima.com/en/artworks/2024/01/tritone-d-g-reflexions-17_7lu.html

[3] Quercy, A. (2025). Computational Image Analysis Standard - MMIDS-CMP-2025 https://multimodal.institute/en/publications/2025/10/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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