AQC0962

Nanopublication — Computational Image Analysis - AQC0962

by Arnaud Quercy · B sus4 - Research on Harmony · 2026

Claim 1: Computational Image Analysis - AQC0962

The artwork B sus4 - Research [1] on Harmony (AQC0962) [2] by Arnaud Quercy [2] underwent comprehensive computational analysis [3] on 2026-03-05. 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]: 1846x2769 pixels. Analysis date: 2026-03-05.

Color Analysis

Rank Color Hex % Family Name
1 2A352B 13.3 yellow-green darkslategray
2 D5D2C7 12.5 yellow lightgray
3 3F583D 11.9 yellow-green darkslategrey
4 C3C1B9 10.8 gray silver
5 929381 10.3 yellow-green gray
6 70A467 9.6 yellow-green grey
7 AAB18F 9.5 yellow-green steel gray
8 D5C7A3 8.9 yellow-orange tan
9 587E51 7.2 yellow-green dimgray
10 F0EBD7 6.0 yellow antiquewhite

Color Families:

Family %
yellow-green 61.9
yellow 18.5
gray 10.8
yellow-orange 8.9

Texture Analysis

Metric Value
Global Roughness 0.234
Mean Local Roughness 0.041
Roughness Uniformity 0.033
Edge Density 0.215
Mean Gradient Magnitude 0.307
Gradient Variance 0.116
Gradient Smoothness 0.0
Directional Coherence 0.006
Pattern Complexity 0.119
Pattern Repetition 1.0
Detail Frequency Ratio 0.668
Spatial Variation 0.127
Texture Consistency 0.82

Brightness & Contrast Analysis

Metric Value
Mean Brightness 0.579
Brightness Variance 0.234
Brightness Uniformity 0.596
Brightness Skewness -0.384
Brightness Entropy 7.629
Rms Contrast 0.234
Michelson Contrast 1.0
Weber Contrast 0.743
Mean Local Contrast 0.044
Contrast Uniformity 0.232
Dynamic Range 1.0
Effective Dynamic Range 0.706
Shadow Percentage 21.968
Midtone Percentage 34.121
Highlight Percentage 43.912
Shadow Clipping 0.001
Highlight Clipping 0.001
Tonal Balance 0.29
Fine Contrast 0.024
Medium Contrast 0.054
Coarse Contrast 0.067
Multiscale Contrast Ratio 0.354
Edge Contrast 0.307
Contrast Clustering 0.18

Spatial Distribution Analysis

Metric Value
Spatial Coherence 0.699
Color Clustering 0.907
Color Transition Smoothness 0.222
Transition Uniformity 0.235
Sharp Transition Ratio 0.1
Transition Directionality 0.007
Mean Saturation 0.203
Saturation Variance 0.02
Low Saturation Ratio 0.757
Medium Saturation Ratio 0.241
High Saturation Ratio 0.002
Saturation Clustering 0.999
Hue Concentration 0.833
Complementary Balance 0.002
Analogous Dominance 0.713
Temperature Bias 0.1

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 (2026). B sus4 - Research on Harmony — Catalog raisonné. https://arnaudquercy.art/en/catalogue-raisonne/AQC0962.html

[2] Quercy, A. (2025). Untitled - Gallery. https://artquamanima.com/en/artworks/2026/03/b-sus4-research-on-harmony_1ym8.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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