AQC0672

Nanopublication — Computational Image Analysis - AQC0672

by Arnaud Quercy · Db Octaves - Reflexions 11 · 2024

Claim 1: Computational Image Analysis - AQC0672

Computational image analysis [3] of artwork Db Octaves [1] - Reflexions 11 (AQC0672) [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]: 2247x3370 pixels. Analysis date: 2026-02-04.

Color Analysis

Rank	Hex	%	Family	Name
1	B9B9B3	18.3	gray	silver
2	C5C3BD	17.5	white	lightgray
3	ABAFAA	15.9	gray	steel gray
4	D1CECA	13.1	white	lightgrey
5	9CA5A0	12.5	gray	steel gray
6	8C9794	9.0	green	lightslategray
7	E1DCD9	5.9	white	gainsboro
8	798683	4.7	green	gray
9	576866	2.2	green	dimgray
10	33322E	1.0	gray	darkslategray
11	815F4B	0.3	orange	dimgray [Accent]
12	3E7883	0.3	blue-green	seagreen [Accent]

Color Families:

Family	%
gray	47.6
white	36.6
green	15.9
orange	0.3
blue-green	0.3

Accent Colors:

Hex	Family	Name	Chroma
815F4B	orange	dimgray	20.2
3E7883	blue-green	seagreen	20.0

Texture Analysis

Metric	Value
Global Roughness	0.114
Mean Local Roughness	0.025
Roughness Uniformity	0.018
Edge Density	0.158
Mean Gradient Magnitude	0.195
Gradient Variance	0.038
Gradient Smoothness	0.001
Directional Coherence	0.005
Pattern Complexity	0.122
Pattern Repetition	1.0
Detail Frequency Ratio	0.636
Spatial Variation	0.063
Texture Consistency	0.668

Brightness & Contrast Analysis

Metric	Value
Mean Brightness	0.699
Brightness Variance	0.114
Brightness Uniformity	0.837
Brightness Skewness	-1.342
Brightness Entropy	6.733
Rms Contrast	0.114
Michelson Contrast	1.0
Weber Contrast	0.319
Mean Local Contrast	0.026
Contrast Uniformity	0.314
Dynamic Range	1.0
Effective Dynamic Range	0.345
Shadow Percentage	1.213
Midtone Percentage	29.804
Highlight Percentage	68.983
Shadow Clipping	0.001
Highlight Clipping	0.0
Tonal Balance	0.0
Fine Contrast	0.015
Medium Contrast	0.032
Coarse Contrast	0.046
Multiscale Contrast Ratio	0.33
Edge Contrast	0.195
Contrast Clustering	0.332

Spatial Distribution Analysis

Metric	Value
Spatial Coherence	0.703
Color Clustering	0.879
Color Transition Smoothness	0.522
Transition Uniformity	0.757
Sharp Transition Ratio	0.1
Transition Directionality	0.007
Mean Saturation	0.064
Saturation Variance	0.002
Low Saturation Ratio	0.991
Medium Saturation Ratio	0.009
High Saturation Ratio	0.0
Saturation Clustering	1.0
Hue Concentration	0.531
Complementary Balance	0.087
Analogous Dominance	0.75
Temperature Bias	-0.511

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). Db Octaves - Reflexions 11 — Catalog raisonné. https://arnaudquercy.art/en/catalogue-raisonne/AQC0672.html

[2] Quercy, A. (2025). Untitled - Gallery. https://artquamanima.com/en/artworks/2024/01/db-octaves-reflexions-11_7hk.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 type	computational analysis
Voice	third person
Epistemic status	empirical measurement
Methodology	computational analysis
Certainty	high

Checksum (SHA-256)

dff4d12d303fe184b9e5f539d2946e3637058244e18b0d9abf4ffb20ed241965