AQC0871

Nanopublication — Computational Image Analysis - AQC0871

Claim 1: Computational Image Analysis - AQC0871

The artwork Db Major [1] - Research on Harmony - Variations 14 (AQC0871) [2] by Arnaud Quercy [2] underwent comprehensive computational analysis [3] on 2025-12-11. Method: k-means clustering with 10 colors extracted. Metrics documented: color distribution, texture analysis, brightness/contrast, spatial patterns.

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]: 1939x2908 pixels. Analysis date: 2025-12-11.

Color Analysis

Rank Color Hex % Family Name
1 7E9DB7 17.4 blue-violet lightslategray
2 715A3F 15.0 orange dark brown
3 6A7790 14.9 blue-violet grayish purple
4 E8837E 11.1 red-orange lightcoral
5 98D2C5 10.4 green lightsteelblue
6 A3957C 7.2 yellow-orange rosybrown
7 C1BEA2 6.9 yellow tan
8 D74E4E 6.5 red-orange indianred
9 E9E2BF 6.1 yellow wheat
10 2A180F 4.4 orange very dark gray
11 C7F1D1 0.3 yellow-green gainsboro [Accent]
12 CC8F9F 0.3 red rosybrown [Accent]
13 A6EAF9 0.3 blue-green paleturquoise [Accent]
14 C9DBEA 0.3 blue gainsboro [Accent]
15 AFAFC8 0.3 violet silver [Accent]

Color Families:

Family %
blue-violet 32.2
orange 19.4
red-orange 17.6
yellow 13.1
green 10.4
yellow-orange 7.2
yellow-green 0.3
red 0.3
blue-green 0.3
blue 0.3
violet 0.3

Accent Colors:

Hex Family Name Chroma
C7F1D1 yellow-green gainsboro 22.8
CC8F9F red rosybrown 25.0
A6EAF9 blue-green paleturquoise 22.8
C9DBEA blue gainsboro 9.5
AFAFC8 violet silver 13.9

Texture Analysis

Metric Value
Global Roughness 0.182
Mean Local Roughness 0.045
Roughness Uniformity 0.037
Edge Density 0.225
Mean Gradient Magnitude 0.354
Gradient Variance 0.157
Gradient Smoothness 0.0
Directional Coherence 0.005
Pattern Complexity 0.12
Pattern Repetition 1.0
Detail Frequency Ratio 0.655
Spatial Variation 0.072
Texture Consistency 0.833

Brightness & Contrast Analysis

Metric Value
Mean Brightness 0.56
Brightness Variance 0.182
Brightness Uniformity 0.676
Brightness Skewness -0.357
Brightness Entropy 7.484
Rms Contrast 0.182
Michelson Contrast 1.0
Weber Contrast 0.555
Mean Local Contrast 0.049
Contrast Uniformity 0.222
Dynamic Range 1.0
Effective Dynamic Range 0.584
Shadow Percentage 8.188
Midtone Percentage 64.623
Highlight Percentage 27.189
Shadow Clipping 0.006
Highlight Clipping 0.022
Tonal Balance 0.173
Fine Contrast 0.024
Medium Contrast 0.06
Coarse Contrast 0.086
Multiscale Contrast Ratio 0.282
Edge Contrast 0.354
Contrast Clustering 0.167

Spatial Distribution Analysis

Metric Value
Spatial Coherence 0.709
Color Clustering 0.516
Color Transition Smoothness 0.109
Transition Uniformity 0.0
Sharp Transition Ratio 0.1
Transition Directionality 0.005
Mean Saturation 0.366
Saturation Variance 0.031
Low Saturation Ratio 0.355
Medium Saturation Ratio 0.604
High Saturation Ratio 0.041
Saturation Clustering 0.997
Hue Concentration 0.169
Complementary Balance 0.258
Analogous Dominance 0.566
Temperature Bias 0.132

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 (2025). Db Major - Research on Harmony - Variations 14 — Catalog raisonné. https://arnaudquercy.art/en/catalogue-raisonne/AQC0871.html

[2] Quercy, A. (2025). Untitled - Gallery. https://artquamanima.com/en/artworks/2025/11/db-major-research-on-harmony-variations-14_hzv.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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