AQC0854

Nanopublication — Computational Image Analysis - AQC0854

by Arnaud Quercy · D Minor - Research on Harmony - Variation 8 · 2025

Claim 1: Computational Image Analysis - AQC0854

Analysis record [3]: D Minor [1] - Research on Harmony - Variation 8 (AQC0854) [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]: 2176x2902 pixels. Analysis date: 2026-02-04.

Color Analysis

Rank Color Hex % Family Name
1 D9D0C0 23.9 yellow-orange lightgray
2 D58A33 18.0 orange peru
3 C07326 15.2 orange chocolate
4 D2C1AC 11.0 yellow-orange silver
5 E1DED6 10.5 white gainsboro
6 E0A661 8.0 orange sandybrown
7 4F4A7E 4.6 violet dusty mauve
8 5C4956 4.0 red-violet dusty mauve
9 37292A 2.6 red-orange very dark gray
10 7D6E83 2.2 red-violet dusty mauve

Color Families:

Family %
orange 41.2
yellow-orange 35.0
white 10.5
red-violet 6.2
violet 4.6
red-orange 2.6

Texture Analysis

Metric Value
Global Roughness 0.185
Mean Local Roughness 0.011
Roughness Uniformity 0.015
Edge Density 0.03
Mean Gradient Magnitude 0.1
Gradient Variance 0.024
Gradient Smoothness 0.0
Directional Coherence 0.018
Pattern Complexity 0.112
Pattern Repetition 1.0
Detail Frequency Ratio 0.607
Spatial Variation 0.127
Texture Consistency 0.401

Brightness & Contrast Analysis

Metric Value
Mean Brightness 0.655
Brightness Variance 0.185
Brightness Uniformity 0.717
Brightness Skewness -0.72
Brightness Entropy 7.155
Rms Contrast 0.185
Michelson Contrast 1.0
Weber Contrast 0.571
Mean Local Contrast 0.013
Contrast Uniformity 0.0
Dynamic Range 1.0
Effective Dynamic Range 0.573
Shadow Percentage 7.975
Midtone Percentage 40.276
Highlight Percentage 51.75
Shadow Clipping 0.0
Highlight Clipping 0.0
Tonal Balance 0.0
Fine Contrast 0.006
Medium Contrast 0.016
Coarse Contrast 0.027
Multiscale Contrast Ratio 0.239
Edge Contrast 0.1
Contrast Clustering 0.599

Spatial Distribution Analysis

Metric Value
Spatial Coherence 0.753
Color Clustering 0.599
Color Transition Smoothness 0.741
Transition Uniformity 0.833
Sharp Transition Ratio 0.1
Transition Directionality 0.024
Mean Saturation 0.399
Saturation Variance 0.094
Low Saturation Ratio 0.514
Medium Saturation Ratio 0.182
High Saturation Ratio 0.304
Saturation Clustering 1.0
Hue Concentration 0.775
Complementary Balance 0.007
Analogous Dominance 0.874
Temperature Bias 0.876

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). D Minor - Research on Harmony - Variation 8 — Catalog raisonné. https://arnaudquercy.art/en/catalogue-raisonne/AQC0854.html

[2] Quercy, A. (2025). Untitled - Gallery. https://artquamanima.com/en/artworks/2025/01/d-minor-research-on-harmony-variation-8_9gc.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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