AQC0624

Nanopublication — Computational Image Analysis - AQC0624

Claim 1: Computational Image Analysis - AQC0624

Computational image analysis [3] of artwork C# minor - Research [1] on Harmony - Variation 1 (AQC0624) [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]: 2277x3415 pixels. Analysis date: 2026-02-04.

Color Analysis

Rank Color Hex % Family Name
1 456784 20.3 blue-violet grayish purple
2 EEB92A 18.9 yellow-orange goldenrod
3 30536B 12.9 blue grayish purple
4 83BFA9 11.2 green darkseagreen
5 4B6654 9.0 yellow-green darkslategray
6 190F09 8.1 orange black
7 628699 8.0 blue blue gray
8 33281D 6.3 orange very dark gray
9 E7CDB3 3.4 orange wheat
10 B5A87C 1.9 yellow-orange ochre
11 F9F0D4 0.3 yellow antiquewhite [Accent]

Color Families:

Family %
blue 20.9
yellow-orange 20.8
blue-violet 20.3
orange 17.8
green 11.2
yellow-green 9.0
yellow 0.3

Accent Colors:

Hex Family Name Chroma
F9F0D4 yellow antiquewhite 15.1

Texture Analysis

Metric Value
Global Roughness 0.22
Mean Local Roughness 0.024
Roughness Uniformity 0.028
Edge Density 0.119
Mean Gradient Magnitude 0.194
Gradient Variance 0.082
Gradient Smoothness 0.0
Directional Coherence 0.046
Pattern Complexity 0.125
Pattern Repetition 1.0
Detail Frequency Ratio 0.636
Spatial Variation 0.124
Texture Consistency 0.482

Brightness & Contrast Analysis

Metric Value
Mean Brightness 0.457
Brightness Variance 0.22
Brightness Uniformity 0.518
Brightness Skewness -0.019
Brightness Entropy 7.217
Rms Contrast 0.22
Michelson Contrast 1.0
Weber Contrast 0.806
Mean Local Contrast 0.026
Contrast Uniformity 0.0
Dynamic Range 1.0
Effective Dynamic Range 0.671
Shadow Percentage 28.4
Midtone Percentage 40.978
Highlight Percentage 30.622
Shadow Clipping 0.016
Highlight Clipping 0.007
Tonal Balance 0.0
Fine Contrast 0.013
Medium Contrast 0.033
Coarse Contrast None
Multiscale Contrast Ratio 1.0
Edge Contrast 0.194
Contrast Clustering 0.518

Spatial Distribution Analysis

Metric Value
Spatial Coherence 0.753
Color Clustering 0.659
Color Transition Smoothness 0.482
Transition Uniformity 0.438
Sharp Transition Ratio 0.1
Transition Directionality 0.058
Mean Saturation 0.51
Saturation Variance 0.041
Low Saturation Ratio 0.135
Medium Saturation Ratio 0.645
High Saturation Ratio 0.22
Saturation Clustering 0.998
Hue Concentration 0.265
Complementary Balance 0.172
Analogous Dominance 0.605
Temperature Bias -0.239

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/AQC0624.html

[2] Quercy, A. (2025). Untitled - Gallery. https://artquamanima.com/en/artworks/2024/01/c-minor-research-on-harmony-variation-1_6yw.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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