AQC0584

Nanopublication — Computational Image Analysis - AQC0584

by Arnaud Quercy · C minor - Research on Harmony - Variation 1 · 2024

Claim 1: Computational Image Analysis - AQC0584

Analysis record [3]: C minor - Research [1] on Harmony - Variation 1 (AQC0584) [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]: 2755x3444 pixels. Analysis date: 2026-02-04.

Color Analysis

Rank Color Hex % Family Name
1 BC0D0F 17.5 red-orange firebrick
2 A5080A 17.3 red-orange darkred
3 CC1C20 15.0 red-orange crimson
4 4A3D6C 12.5 violet dusty mauve
5 D92F30 10.6 red-orange brown
6 AE2026 9.4 red-orange burnt sienna
7 1F072D 7.8 red-violet very dark purple
8 5E597D 6.0 violet dusty mauve
9 DB5457 2.7 red-orange indianred
10 EF9598 1.1 red-orange darksalmon
11 CC5014 0.3 orange chocolate [Accent]
12 883853 0.3 red burnt sienna [Accent]

Color Families:

Family %
red-orange 73.6
violet 18.5
red-violet 7.8
orange 0.3
red 0.3

Accent Colors:

Hex Family Name Chroma
CC5014 orange chocolate 72.3
883853 red burnt sienna 37.0

Texture Analysis

Metric Value
Global Roughness 0.103
Mean Local Roughness 0.039
Roughness Uniformity 0.03
Edge Density 0.209
Mean Gradient Magnitude 0.296
Gradient Variance 0.092
Gradient Smoothness 0.0
Directional Coherence 0.019
Pattern Complexity 0.166
Pattern Repetition 1.0
Detail Frequency Ratio 0.71
Spatial Variation 0.052
Texture Consistency 0.64

Brightness & Contrast Analysis

Metric Value
Mean Brightness 0.284
Brightness Variance 0.103
Brightness Uniformity 0.637
Brightness Skewness 0.735
Brightness Entropy 6.432
Rms Contrast 0.103
Michelson Contrast 1.0
Weber Contrast 0.5
Mean Local Contrast 0.046
Contrast Uniformity 0.263
Dynamic Range 1.0
Effective Dynamic Range 0.361
Shadow Percentage 72.993
Midtone Percentage 26.216
Highlight Percentage 0.792
Shadow Clipping 0.0
Highlight Clipping 0.0
Tonal Balance 0.0
Fine Contrast 0.02
Medium Contrast 0.054
Coarse Contrast 0.053
Multiscale Contrast Ratio 0.384
Edge Contrast 0.296
Contrast Clustering 0.36

Spatial Distribution Analysis

Metric Value
Spatial Coherence 0.722
Color Clustering 0.397
Color Transition Smoothness 0.362
Transition Uniformity 0.535
Sharp Transition Ratio 0.1
Transition Directionality 0.019
Mean Saturation 0.78
Saturation Variance 0.046
Low Saturation Ratio 0.061
Medium Saturation Ratio 0.171
High Saturation Ratio 0.768
Saturation Clustering 0.997
Hue Concentration 0.741
Complementary Balance 0.0
Analogous Dominance 0.761
Temperature Bias 0.701

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 minor - Research on Harmony - Variation 1 — Catalog raisonné. https://arnaudquercy.art/en/catalogue-raisonne/AQC0584.html

[2] Quercy, A. (2025). Untitled - Gallery. https://artquamanima.com/en/artworks/2024/01/c-minor-research-on-harmony-variation-1_6jc.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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