AQC0874

Nanopublication — Computational Image Analysis - AQC0874

by Arnaud Quercy · Eb Minor - Research on Harmony - Variations 8 · 2025

Claim 1: Computational Image Analysis - AQC0874

Analysis record [3]: Eb Minor [1] - Research on Harmony - Variations 8 (AQC0874) [2] by Arnaud Quercy [2]. Method: k-means. Parameters: 10 colors. Metrics: color distribution, texture, brightness, spatial patterns. Completed: 2025-12-11.

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]: 2115x3173 pixels. Analysis date: 2025-12-11.

Color Analysis

Rank	Hex	%	Family	Name
1	DBC4DA	20.4	red-violet	thistle
2	B086BF	14.5	red-violet	steel gray
3	C8A4D3	13.0	red-violet	plum
4	9770A9	12.9	red-violet	dusty mauve
5	F1DDEE	10.6	red-violet	lavender
6	5A5056	8.2	red-violet	dusty mauve
7	74578E	6.7	violet	dusty mauve
8	99D586	6.0	yellow-green	darkseagreen
9	7EB770	3.9	yellow-green	gray
10	1F1820	3.7	red-violet	very dark gray

Color Families:

Family	%
red-violet	83.3
yellow-green	9.9
violet	6.7

Texture Analysis

Metric	Value
Global Roughness	0.203
Mean Local Roughness	0.051
Roughness Uniformity	0.043
Edge Density	0.219
Mean Gradient Magnitude	0.392
Gradient Variance	0.204
Gradient Smoothness	0.0
Directional Coherence	0.013
Pattern Complexity	0.122
Pattern Repetition	1.0
Detail Frequency Ratio	0.655
Spatial Variation	0.083
Texture Consistency	0.896

Brightness & Contrast Analysis

Metric	Value
Mean Brightness	0.633
Brightness Variance	0.203
Brightness Uniformity	0.679
Brightness Skewness	-0.718
Brightness Entropy	7.581
Rms Contrast	0.203
Michelson Contrast	1.0
Weber Contrast	0.595
Mean Local Contrast	0.055
Contrast Uniformity	0.192
Dynamic Range	1.0
Effective Dynamic Range	0.635
Shadow Percentage	8.51
Midtone Percentage	41.648
Highlight Percentage	49.842
Shadow Clipping	0.033
Highlight Clipping	0.018
Tonal Balance	0.259
Fine Contrast	0.029
Medium Contrast	0.067
Coarse Contrast	0.095
Multiscale Contrast Ratio	0.308
Edge Contrast	0.392
Contrast Clustering	0.104

Spatial Distribution Analysis

Metric	Value
Spatial Coherence	0.673
Color Clustering	0.803
Color Transition Smoothness	0.016
Transition Uniformity	0.0
Sharp Transition Ratio	0.1
Transition Directionality	0.013
Mean Saturation	0.246
Saturation Variance	0.022
Low Saturation Ratio	0.643
Medium Saturation Ratio	0.347
High Saturation Ratio	0.01
Saturation Clustering	0.997
Hue Concentration	0.62
Complementary Balance	0.18
Analogous Dominance	0.808
Temperature Bias	0.021

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 (2025). Eb Minor - Research on Harmony - Variations 8 — Catalog raisonné. https://arnaudquercy.art/en/catalogue-raisonne/AQC0874.html

[2] Quercy, A. (2025). Untitled - Gallery. https://artquamanima.com/en/artworks/2025/11/eb-minor-research-on-harmony-variations-8_i0y.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)

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