AQC0708

Nanopublication — Computational Image Analysis - AQC0708

by Arnaud Quercy · Nocturnal Visions · 2024

Claim 1: Computational Image Analysis - AQC0708

The artwork Nocturnal [1] Visions (AQC0708) [2] by Arnaud Quercy [2] underwent comprehensive computational analysis [3] on 2026-02-04. Method: k-means clustering with 10 colors extracted. Metrics documented: color distribution, texture analysis, brightness/contrast, spatial patterns.

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]: 1400x1400 pixels. Analysis date: 2026-02-04.

Color Analysis

Rank Color Hex % Family Name
1 333333 36.1 gray darkslategray
2 7F3951 15.5 red burnt sienna
3 6F2E47 13.3 red dusty mauve
4 490D32 11.0 red very dark red
5 57163C 8.8 red darkslategrey
6 904760 5.9 red dimgray
7 E76C05 5.8 orange chocolate
8 81549C 2.6 red-violet blue gray
9 F8F7F7 0.5 white white
10 CFB6BF 0.4 red silver
11 7B77BD 0.3 violet slateblue [Accent]

Color Families:

Family %
red 55.0
gray 36.1
orange 5.8
red-violet 2.6
white 0.5
violet 0.3

Accent Colors:

Hex Family Name Chroma
7B77BD violet slateblue 40.2

Texture Analysis

Metric Value
Global Roughness 0.118
Mean Local Roughness 0.023
Roughness Uniformity 0.033
Edge Density 0.084
Mean Gradient Magnitude 0.146
Gradient Variance 0.089
Gradient Smoothness 0.0
Directional Coherence 0.071
Pattern Complexity 0.144
Pattern Repetition 1.0
Detail Frequency Ratio 0.661
Spatial Variation 0.07
Texture Consistency 0.425

Brightness & Contrast Analysis

Metric Value
Mean Brightness 0.262
Brightness Variance 0.118
Brightness Uniformity 0.552
Brightness Skewness 2.315
Brightness Entropy 5.498
Rms Contrast 0.118
Michelson Contrast 0.947
Weber Contrast 0.604
Mean Local Contrast 0.021
Contrast Uniformity 0.0
Dynamic Range 0.973
Effective Dynamic Range 0.373
Shadow Percentage 80.78
Midtone Percentage 18.32
Highlight Percentage 0.9
Shadow Clipping 0.0
Highlight Clipping 0.432
Tonal Balance 0.0
Fine Contrast 0.015
Medium Contrast 0.027
Coarse Contrast 0.034
Multiscale Contrast Ratio 0.426
Edge Contrast 0.146
Contrast Clustering 0.575

Spatial Distribution Analysis

Metric Value
Spatial Coherence 0.777
Color Clustering 0.0
Color Transition Smoothness 0.608
Transition Uniformity 0.419
Sharp Transition Ratio 0.1
Transition Directionality 0.021
Mean Saturation 0.422
Saturation Variance 0.114
Low Saturation Ratio 0.373
Medium Saturation Ratio 0.392
High Saturation Ratio 0.235
Saturation Clustering 0.998
Hue Concentration 0.935
Complementary Balance 0.0
Analogous Dominance 0.947
Temperature Bias 0.957

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). Nocturnal Visions — Catalog raisonné. https://arnaudquercy.art/en/catalogue-raisonne/AQC0708.html

[2] Quercy, A. (2025). Untitled - Gallery. https://artquamanima.com/en/artworks/2024/01/nocturnal-visions_7vk.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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