AQC0612

Nanopublication — Computational Image Analysis - AQC0612

by Arnaud Quercy · Bb minor - Research on Harmony · 2024

Claim 1: Computational Image Analysis - AQC0612

Analysis record [3]: Bb minor - Research [1] on Harmony (AQC0612) [2] by Arnaud Quercy [2]. Method: k-means. Parameters: 10 colors. Metrics: color distribution, texture, brightness, spatial patterns. Completed: 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]: 1885x2591 pixels. Analysis date: 2026-02-04.

Color Analysis

Rank Color Hex % Family Name
1 66538B 15.8 violet dusty mauve
2 380E20 15.5 red very dark red
3 5E4C7C 15.2 violet dusty mauve
4 53426D 13.4 violet dusty mauve
5 2D0817 12.5 red very dark red
6 368E96 11.7 blue-green steelblue
7 6F5C98 8.6 violet dusty mauve
8 41172E 5.3 red very dark red
9 A48B99 1.5 red-violet rosybrown
10 C1B6BD 0.5 red-violet silver
11 07142D 0.3 blue-violet very dark indigo [Accent]
12 062029 0.3 blue very dark gray [Accent]

Color Families:

Family %
violet 52.9
red 33.3
blue-green 11.7
red-violet 2.1
blue-violet 0.3
blue 0.3

Accent Colors:

Hex Family Name Chroma
07142D blue-violet very dark indigo 19.4
062029 blue very dark gray 11.4

Texture Analysis

Metric Value
Global Roughness 0.142
Mean Local Roughness 0.014
Roughness Uniformity 0.017
Edge Density 0.026
Mean Gradient Magnitude 0.096
Gradient Variance 0.024
Gradient Smoothness 0.0
Directional Coherence 0.072
Pattern Complexity 0.132
Pattern Repetition 1.0
Detail Frequency Ratio 0.669
Spatial Variation 0.112
Texture Consistency 0.374

Brightness & Contrast Analysis

Metric Value
Mean Brightness 0.289
Brightness Variance 0.142
Brightness Uniformity 0.509
Brightness Skewness -0.11
Brightness Entropy 6.444
Rms Contrast 0.142
Michelson Contrast 1.0
Weber Contrast 0.8
Mean Local Contrast 0.014
Contrast Uniformity 0.0
Dynamic Range 1.0
Effective Dynamic Range 0.388
Shadow Percentage 50.192
Midtone Percentage 49.263
Highlight Percentage 0.545
Shadow Clipping 0.002
Highlight Clipping 0.0
Tonal Balance 0.0
Fine Contrast 0.008
Medium Contrast 0.018
Coarse Contrast None
Multiscale Contrast Ratio 1.0
Edge Contrast 0.096
Contrast Clustering 0.626

Spatial Distribution Analysis

Metric Value
Spatial Coherence 0.749
Color Clustering 0.713
Color Transition Smoothness 0.733
Transition Uniformity 0.824
Sharp Transition Ratio 0.1
Transition Directionality 0.083
Mean Saturation 0.539
Saturation Variance 0.035
Low Saturation Ratio 0.025
Medium Saturation Ratio 0.71
High Saturation Ratio 0.265
Saturation Clustering 0.999
Hue Concentration 0.701
Complementary Balance 0.0
Analogous Dominance 0.854
Temperature Bias 0.209

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). Bb minor - Research on Harmony — Catalog raisonné. https://arnaudquercy.art/en/catalogue-raisonne/AQC0612.html

[2] Quercy, A. (2025). Untitled - Gallery. https://artquamanima.com/en/artworks/2024/01/bb-minor-research-on-harmony_6u8.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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