AQC0883

Nanopublication — Computational Image Analysis - AQC0883

by Arnaud Quercy · G Minor - Research on Harmony - Variations 11 · 2025

Claim 1: Computational Image Analysis - AQC0883

Analysis record [3]: G Minor [1] - Research on Harmony - Variations 11 (AQC0883) [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]: 1731x2597 pixels. Analysis date: 2025-12-11.

Color Analysis

Rank Color Hex % Family Name
1 E9C792 19.1 yellow-orange burlywood
2 D5AABE 17.0 red silver
3 EEAB5B 16.2 orange sandybrown
4 C488AD 8.9 red-violet rosybrown
5 EF8A35 8.8 orange coral
6 E68012 6.8 orange chocolate
7 EFDCD1 6.5 orange antiquewhite
8 5D4E46 6.3 orange dark brown
9 A87298 5.7 red-violet dusty mauve
10 37241F 4.6 red-orange very dark gray

Color Families:

Family %
orange 44.7
yellow-orange 19.1
red 17.0
red-violet 14.6
red-orange 4.6

Texture Analysis

Metric Value
Global Roughness 0.173
Mean Local Roughness 0.025
Roughness Uniformity 0.032
Edge Density 0.081
Mean Gradient Magnitude 0.21
Gradient Variance 0.117
Gradient Smoothness 0.0
Directional Coherence 0.013
Pattern Complexity 0.111
Pattern Repetition 1.0
Detail Frequency Ratio 0.621
Spatial Variation 0.047
Texture Consistency 0.843

Brightness & Contrast Analysis

Metric Value
Mean Brightness 0.658
Brightness Variance 0.173
Brightness Uniformity 0.736
Brightness Skewness -1.346
Brightness Entropy 7.117
Rms Contrast 0.173
Michelson Contrast 1.0
Weber Contrast 0.571
Mean Local Contrast 0.029
Contrast Uniformity 0.0
Dynamic Range 1.0
Effective Dynamic Range 0.604
Shadow Percentage 8.724
Midtone Percentage 31.166
Highlight Percentage 60.11
Shadow Clipping 0.0
Highlight Clipping 0.001
Tonal Balance 0.0
Fine Contrast 0.013
Medium Contrast 0.036
Coarse Contrast 0.059
Multiscale Contrast Ratio 0.229
Edge Contrast 0.21
Contrast Clustering 0.157

Spatial Distribution Analysis

Metric Value
Spatial Coherence 0.687
Color Clustering 0.535
Color Transition Smoothness 0.468
Transition Uniformity 0.214
Sharp Transition Ratio 0.1
Transition Directionality 0.015
Mean Saturation 0.43
Saturation Variance 0.056
Low Saturation Ratio 0.341
Medium Saturation Ratio 0.496
High Saturation Ratio 0.163
Saturation Clustering 0.998
Hue Concentration 0.863
Complementary Balance 0.0
Analogous Dominance 0.912
Temperature Bias 0.995

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). G Minor - Research on Harmony - Variations 11 — Catalog raisonné. https://arnaudquercy.art/en/catalogue-raisonne/AQC0883.html

[2] Quercy, A. (2025). Untitled - Gallery. https://artquamanima.com/en/artworks/2025/11/g-minor-research-on-harmony-variations-11_i47.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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