AQC0755

Nanopublication — Computational Image Analysis - AQC0755

by Arnaud Quercy · D Minor - Research on Harmony - Variation 2 · 2024

Claim 1: Computational Image Analysis - AQC0755

The artwork D Minor [1] - Research on Harmony - Variation 2 (AQC0755) [2] by Arnaud Quercy [2] underwent comprehensive computational analysis [3] on 2026-02-04. Method: k-means clustering with 10 colors extracted. Metrics documented: color distribution, texture analysis, brightness/contrast, spatial patterns.

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]: 2968x3958 pixels. Analysis date: 2026-02-04.

Color Analysis

Rank Color Hex % Family Name
1 ADA69C 17.1 yellow-orange steel gray
2 DC9B49 16.2 orange peru
3 E3861B 13.5 orange goldenrod
4 DBA56C 11.5 orange darksalmon
5 1E1918 11.2 gray black
6 7B3933 7.3 red-orange russet
7 C3942A 6.9 yellow-orange darkgoldenrod
8 C4B9AD 6.0 yellow-orange silver
9 AF7742 5.6 orange burnt sienna
10 9F495F 4.6 red indianred
11 5D5834 0.3 yellow dark brown [Accent]

Color Families:

Family %
orange 46.8
yellow-orange 30.0
gray 11.2
red-orange 7.3
red 4.6
yellow 0.3

Accent Colors:

Hex Family Name Chroma
5D5834 yellow dark brown 22.4

Texture Analysis

Metric Value
Global Roughness 0.194
Mean Local Roughness 0.017
Roughness Uniformity 0.015
Edge Density 0.081
Mean Gradient Magnitude 0.165
Gradient Variance 0.042
Gradient Smoothness 0.0
Directional Coherence 0.009
Pattern Complexity 0.12
Pattern Repetition 1.0
Detail Frequency Ratio 0.593
Spatial Variation 0.146
Texture Consistency 0.535

Brightness & Contrast Analysis

Metric Value
Mean Brightness 0.542
Brightness Variance 0.194
Brightness Uniformity 0.643
Brightness Skewness -1.289
Brightness Entropy 6.857
Rms Contrast 0.194
Michelson Contrast 1.0
Weber Contrast 0.799
Mean Local Contrast 0.021
Contrast Uniformity 0.092
Dynamic Range 1.0
Effective Dynamic Range 0.627
Shadow Percentage 17.097
Midtone Percentage 55.762
Highlight Percentage 27.14
Shadow Clipping 0.001
Highlight Clipping 0.0
Tonal Balance 0.0
Fine Contrast 0.008
Medium Contrast 0.026
Coarse Contrast 0.044
Multiscale Contrast Ratio 0.189
Edge Contrast 0.165
Contrast Clustering 0.465

Spatial Distribution Analysis

Metric Value
Spatial Coherence 0.767
Color Clustering 0.459
Color Transition Smoothness 0.584
Transition Uniformity 0.72
Sharp Transition Ratio 0.1
Transition Directionality 0.01
Mean Saturation 0.485
Saturation Variance 0.096
Low Saturation Ratio 0.324
Medium Saturation Ratio 0.396
High Saturation Ratio 0.28
Saturation Clustering 0.999
Hue Concentration 0.96
Complementary Balance 0.0
Analogous Dominance 0.993
Temperature Bias 0.999

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). D Minor - Research on Harmony - Variation 2 — Catalog raisonné. https://arnaudquercy.art/en/catalogue-raisonne/AQC0755.html

[2] Quercy, A. (2025). Untitled - Gallery. https://artquamanima.com/en/artworks/2024/01/d-minor-research-on-harmony-variation-2_8du.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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