AQC0771

Nanopublication — Computational Image Analysis - AQC0771

Claim 1: Computational Image Analysis - AQC0771

Computational image analysis [3] of artwork Bb Major [1] - Research on Harmony - Variation 5 (AQC0771) [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]: 2441x3662 pixels. Analysis date: 2026-02-04.

Color Analysis

Rank Color Hex % Family Name
1 1C1721 20.8 violet black
2 B9A4D6 18.1 violet lightsteelblue
3 823151 13.6 red dusty mauve
4 4196B7 10.9 blue steelblue
5 5CB1CC 8.2 blue mediumturquoise
6 BFBCAE 6.8 yellow silver
7 9E9A90 6.1 yellow-orange steel gray
8 4A3659 5.7 violet dusty mauve
9 AA4A78 5.4 red indianred
10 7162A2 4.5 violet dusty mauve
11 0D3861 0.3 blue-violet grayish purple [Accent]
12 5E3C2E 0.3 orange dark brown [Accent]
13 775950 0.3 red-orange dimgray [Accent]
14 8FC1C4 0.3 blue-green skyblue [Accent]
15 D7D9CD 0.3 yellow-green lightgray [Accent]

Color Families:

Family %
violet 49.2
red 19.0
blue 19.0
yellow 6.8
yellow-orange 6.1
blue-violet 0.3
orange 0.3
red-orange 0.3
blue-green 0.3
yellow-green 0.3

Accent Colors:

Hex Family Name Chroma
0D3861 blue-violet grayish purple 28.1
5E3C2E orange dark brown 19.8
775950 red-orange dimgray 14.9
8FC1C4 blue-green skyblue 17.5
D7D9CD yellow-green lightgray 6.7

Texture Analysis

Metric Value
Global Roughness 0.228
Mean Local Roughness 0.009
Roughness Uniformity 0.01
Edge Density 0.033
Mean Gradient Magnitude 0.101
Gradient Variance 0.022
Gradient Smoothness 0.0
Directional Coherence 0.035
Pattern Complexity 0.104
Pattern Repetition 1.0
Detail Frequency Ratio 0.568
Spatial Variation 0.191
Texture Consistency 0.487

Brightness & Contrast Analysis

Metric Value
Mean Brightness 0.436
Brightness Variance 0.228
Brightness Uniformity 0.478
Brightness Skewness -0.229
Brightness Entropy 7.442
Rms Contrast 0.228
Michelson Contrast 1.0
Weber Contrast 0.857
Mean Local Contrast 0.012
Contrast Uniformity 0.0
Dynamic Range 0.992
Effective Dynamic Range 0.663
Shadow Percentage 36.892
Midtone Percentage 41.158
Highlight Percentage 21.949
Shadow Clipping 0.0
Highlight Clipping 0.0
Tonal Balance 0.185
Fine Contrast 0.005
Medium Contrast 0.015
Coarse Contrast 0.03
Multiscale Contrast Ratio 0.151
Edge Contrast 0.101
Contrast Clustering 0.513

Spatial Distribution Analysis

Metric Value
Spatial Coherence 0.78
Color Clustering 0.726
Color Transition Smoothness 0.734
Transition Uniformity 0.85
Sharp Transition Ratio 0.1
Transition Directionality 0.048
Mean Saturation 0.392
Saturation Variance 0.041
Low Saturation Ratio 0.431
Medium Saturation Ratio 0.531
High Saturation Ratio 0.039
Saturation Clustering 1.0
Hue Concentration 0.665
Complementary Balance 0.007
Analogous Dominance 0.718
Temperature Bias 0.019

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). Bb Major - Research on Harmony - Variation 5 — Catalog raisonné. https://arnaudquercy.art/en/catalogue-raisonne/AQC0771.html

[2] Quercy, A. (2025). Untitled - Gallery. https://artquamanima.com/en/artworks/2024/01/bb-major-research-on-harmony-variation-5_8k2.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)

4fb08f99953e461820cbdea79dcc88494373099f7361cc75701154af2385fc65