AQC0604

Nanopublication — Computational Image Analysis - AQC0604

by Arnaud Quercy · G Major - Research on Harmony · 2024

Claim 1: Computational Image Analysis - AQC0604

Analysis record [3]: G Major [1] - Research on Harmony (AQC0604) [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]: 2684x3578 pixels. Analysis date: 2026-02-04.

Color Analysis

Rank Color Hex % Family Name
1 A92F16 23.3 red-orange firebrick
2 EB6008 21.9 orange chocolate
3 C63B19 18.0 red-orange brown
4 479A4A 14.0 yellow-green seagreen
5 C84D36 11.0 red-orange indianred
6 E36F5B 4.6 red-orange coral
7 AAC567 3.0 yellow-green ochre
8 78BD39 2.3 yellow-green yellowgreen
9 EDAF98 1.5 orange burlywood
10 362212 0.5 orange very dark orange
11 CDCBB0 0.3 yellow silver [Accent]
12 79682A 0.3 yellow-orange dark brown [Accent]

Color Families:

Family %
red-orange 56.8
orange 23.9
yellow-green 19.3
yellow 0.3
yellow-orange 0.3

Accent Colors:

Hex Family Name Chroma
CDCBB0 yellow silver 14.6
79682A yellow-orange dark brown 36.0

Texture Analysis

Metric Value
Global Roughness 0.11
Mean Local Roughness 0.026
Roughness Uniformity 0.034
Edge Density 0.1
Mean Gradient Magnitude 0.188
Gradient Variance 0.087
Gradient Smoothness 0.0
Directional Coherence 0.077
Pattern Complexity 0.119
Pattern Repetition 1.0
Detail Frequency Ratio 0.695
Spatial Variation 0.063
Texture Consistency 0.62

Brightness & Contrast Analysis

Metric Value
Mean Brightness 0.44
Brightness Variance 0.11
Brightness Uniformity 0.751
Brightness Skewness 0.578
Brightness Entropy 6.662
Rms Contrast 0.11
Michelson Contrast 1.0
Weber Contrast 0.448
Mean Local Contrast 0.027
Contrast Uniformity 0.0
Dynamic Range 1.0
Effective Dynamic Range 0.357
Shadow Percentage 17.817
Midtone Percentage 77.464
Highlight Percentage 4.718
Shadow Clipping 0.001
Highlight Clipping 0.001
Tonal Balance 0.0
Fine Contrast 0.015
Medium Contrast 0.033
Coarse Contrast None
Multiscale Contrast Ratio 1.0
Edge Contrast 0.188
Contrast Clustering 0.38

Spatial Distribution Analysis

Metric Value
Spatial Coherence 0.731
Color Clustering 0.346
Color Transition Smoothness 0.556
Transition Uniformity 0.503
Sharp Transition Ratio 0.1
Transition Directionality 0.092
Mean Saturation 0.791
Saturation Variance 0.03
Low Saturation Ratio 0.007
Medium Saturation Ratio 0.271
High Saturation Ratio 0.722
Saturation Clustering 0.998
Hue Concentration 0.795
Complementary Balance 0.0
Analogous Dominance 0.808
Temperature Bias 0.675

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). G Major - Research on Harmony — Catalog raisonné. https://arnaudquercy.art/en/catalogue-raisonne/AQC0604.html

[2] Quercy, A. (2025). Untitled - Gallery. https://artquamanima.com/en/artworks/2024/01/g-major-research-on-harmony_6r4.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)

cdc26163a8086435aab59401ebdfc968d077c1a3196dec907d761010484e2546