AQC0630

Nanopublication — Computational Image Analysis - AQC0630

Claim 1: Computational Image Analysis - AQC0630

Analysis record [3]: Eb minor - Research [1] on Harmony - Variation 2 (AQC0630) [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]: 2267x3401 pixels. Analysis date: 2026-02-04.

Color Analysis

Rank Color Hex % Family Name
1 4757A5 22.0 violet darkslateblue
2 323745 18.1 blue-violet grayish purple
3 6264B1 12.9 violet slateblue
4 4D525F 12.1 blue-violet grayish purple
5 1A1B17 8.7 gray black
6 6A6D7A 6.6 blue-violet grayish purple
7 5BBA69 6.4 yellow-green mediumseagreen
8 77D583 5.6 yellow-green darkseagreen
9 8994B5 3.9 blue-violet lightslategray
10 D1E1EC 3.8 blue gainsboro
11 96773B 0.3 yellow-orange burnt sienna [Accent]
12 F0FDFA 0.3 green white [Accent]

Color Families:

Family %
blue-violet 40.7
violet 34.9
yellow-green 12.0
gray 8.7
blue 3.8
yellow-orange 0.3
green 0.3

Accent Colors:

Hex Family Name Chroma
96773B yellow-orange burnt sienna 37.3
F0FDFA green white 5.0

Texture Analysis

Metric Value
Global Roughness 0.183
Mean Local Roughness 0.029
Roughness Uniformity 0.032
Edge Density 0.13
Mean Gradient Magnitude 0.253
Gradient Variance 0.115
Gradient Smoothness 0.0
Directional Coherence 0.02
Pattern Complexity 0.118
Pattern Repetition 1.0
Detail Frequency Ratio 0.626
Spatial Variation 0.101
Texture Consistency 0.666

Brightness & Contrast Analysis

Metric Value
Mean Brightness 0.382
Brightness Variance 0.183
Brightness Uniformity 0.523
Brightness Skewness 0.79
Brightness Entropy 7.335
Rms Contrast 0.183
Michelson Contrast 1.0
Weber Contrast 0.726
Mean Local Contrast 0.033
Contrast Uniformity 0.032
Dynamic Range 1.0
Effective Dynamic Range 0.604
Shadow Percentage 36.07
Midtone Percentage 55.685
Highlight Percentage 8.245
Shadow Clipping 0.035
Highlight Clipping 0.018
Tonal Balance 0.0
Fine Contrast 0.016
Medium Contrast 0.042
Coarse Contrast 0.072
Multiscale Contrast Ratio 0.216
Edge Contrast 0.253
Contrast Clustering 0.334

Spatial Distribution Analysis

Metric Value
Spatial Coherence 0.722
Color Clustering 0.646
Color Transition Smoothness 0.325
Transition Uniformity 0.244
Sharp Transition Ratio 0.1
Transition Directionality 0.023
Mean Saturation 0.392
Saturation Variance 0.032
Low Saturation Ratio 0.322
Medium Saturation Ratio 0.664
High Saturation Ratio 0.015
Saturation Clustering 0.998
Hue Concentration 0.637
Complementary Balance 0.078
Analogous Dominance 0.735
Temperature Bias -0.73

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). Eb minor - Research on Harmony - Variation 2 — Catalog raisonné. https://arnaudquercy.art/en/catalogue-raisonne/AQC0630.html

[2] Quercy, A. (2025). Untitled - Gallery. https://artquamanima.com/en/artworks/2024/01/eb-minor-research-on-harmony-variation-2_718.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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