AQC0556

Nanopublication — Computational Image Analysis - AQC0556

Claim 1: Computational Image Analysis - AQC0556

Computational image analysis [3] of artwork C Major9 - Research [1] on Harmony - Variation 8 (AQC0556) [2] by Arnaud Quercy [2] using k-means clustering method with 10 color extraction parameters. Analysis includes color distribution, texture metrics, brightness/contrast measurements, and spatial pattern characterization. Analysis completed on 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]: 3024x4032 pixels. Analysis date: 2026-02-04.

Color Analysis

Rank Color Hex % Family Name
1 2D2329 17.1 red-violet very dark gray
2 44373C 12.7 red dusty mauve
3 D29178 12.5 orange darksalmon
4 CA4F1C 11.7 orange chocolate
5 E9A991 11.6 orange burlywood
6 180F14 11.1 red-violet black
7 E76D3A 10.1 orange tomato
8 605054 6.8 red dimgray
9 D5CCC0 3.6 yellow-orange lightgray
10 887577 2.9 red gray
11 ECE6D3 0.3 yellow antiquewhite [Accent]
12 8D4331 0.3 red-orange burnt sienna [Accent]

Color Families:

Family %
orange 45.8
red-violet 28.2
red 22.4
yellow-orange 3.6
yellow 0.3
red-orange 0.3

Accent Colors:

Hex Family Name Chroma
ECE6D3 yellow antiquewhite 10.0
8D4331 red-orange burnt sienna 39.1

Texture Analysis

Metric Value
Global Roughness 0.236
Mean Local Roughness 0.025
Roughness Uniformity 0.018
Edge Density 0.144
Mean Gradient Magnitude 0.245
Gradient Variance 0.055
Gradient Smoothness 0.044
Directional Coherence 0.015
Pattern Complexity 0.116
Pattern Repetition 1.0
Detail Frequency Ratio 0.596
Spatial Variation 0.172
Texture Consistency 0.563

Brightness & Contrast Analysis

Metric Value
Mean Brightness 0.4
Brightness Variance 0.236
Brightness Uniformity 0.409
Brightness Skewness 0.141
Brightness Entropy 7.659
Rms Contrast 0.236
Michelson Contrast 1.0
Weber Contrast 0.852
Mean Local Contrast 0.029
Contrast Uniformity 0.32
Dynamic Range 1.0
Effective Dynamic Range 0.694
Shadow Percentage 44.414
Midtone Percentage 37.382
Highlight Percentage 18.203
Shadow Clipping 0.017
Highlight Clipping 0.001
Tonal Balance 0.374
Fine Contrast 0.013
Medium Contrast 0.036
Coarse Contrast 0.069
Multiscale Contrast Ratio 0.193
Edge Contrast 0.245
Contrast Clustering 0.437

Spatial Distribution Analysis

Metric Value
Spatial Coherence 0.762
Color Clustering 0.623
Color Transition Smoothness 0.342
Transition Uniformity 0.591
Sharp Transition Ratio 0.1
Transition Directionality 0.017
Mean Saturation 0.414
Saturation Variance 0.061
Low Saturation Ratio 0.378
Medium Saturation Ratio 0.411
High Saturation Ratio 0.211
Saturation Clustering 0.998
Hue Concentration 0.852
Complementary Balance 0.0
Analogous Dominance 0.903
Temperature Bias 0.917

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). C Major9 - Research on Harmony - Variation 8 — Catalog raisonné. https://arnaudquercy.art/en/catalogue-raisonne/AQC0556.html

[2] Quercy, A. (2025). Untitled - Gallery. https://artquamanima.com/en/artworks/2024/01/c-major9-research-on-harmony-variation-8_68g.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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