AQC0654

Nanopublication — Computational Image Analysis - AQC0654

Claim 1: Computational Image Analysis - AQC0654

Computational image analysis [3] of artwork D Major [1] - Research on Harmony - Variation 3 (AQC0654) [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]: 2495x3327 pixels. Analysis date: 2026-02-04.

Color Analysis

Rank Color Hex % Family Name
1 D7CBB7 17.7 yellow-orange silver
2 E8DFCD 12.4 yellow-orange gainsboro
3 E2A54B 11.8 yellow-orange sandybrown
4 130D0C 10.7 black black
5 879388 10.7 yellow-green gray
6 242631 9.7 violet very dark gray
7 697B72 9.0 yellow-green dimgray
8 C2B5A0 8.8 yellow-orange tan
9 6B4923 4.9 orange russet
10 B2802B 4.3 yellow-orange darkgoldenrod
11 666146 0.3 yellow dark brown [Accent]

Color Families:

Family %
yellow-orange 55.0
yellow-green 19.7
black 10.7
violet 9.7
orange 4.9
yellow 0.3

Accent Colors:

Hex Family Name Chroma
666146 yellow dark brown 16.3

Texture Analysis

Metric Value
Global Roughness 0.275
Mean Local Roughness 0.031
Roughness Uniformity 0.028
Edge Density 0.171
Mean Gradient Magnitude 0.246
Gradient Variance 0.098
Gradient Smoothness 0.0
Directional Coherence 0.018
Pattern Complexity 0.114
Pattern Repetition 1.0
Detail Frequency Ratio 0.637
Spatial Variation 0.143
Texture Consistency 0.656

Brightness & Contrast Analysis

Metric Value
Mean Brightness 0.555
Brightness Variance 0.275
Brightness Uniformity 0.505
Brightness Skewness -0.614
Brightness Entropy 7.666
Rms Contrast 0.275
Michelson Contrast 1.0
Weber Contrast 0.881
Mean Local Contrast 0.035
Contrast Uniformity 0.14
Dynamic Range 1.0
Effective Dynamic Range 0.831
Shadow Percentage 23.315
Midtone Percentage 31.449
Highlight Percentage 45.236
Shadow Clipping 0.045
Highlight Clipping 0.011
Tonal Balance 0.333
Fine Contrast 0.017
Medium Contrast 0.043
Coarse Contrast 0.06
Multiscale Contrast Ratio 0.289
Edge Contrast 0.246
Contrast Clustering 0.344

Spatial Distribution Analysis

Metric Value
Spatial Coherence 0.73
Color Clustering 0.763
Color Transition Smoothness 0.375
Transition Uniformity 0.325
Sharp Transition Ratio 0.1
Transition Directionality 0.02
Mean Saturation 0.316
Saturation Variance 0.066
Low Saturation Ratio 0.638
Medium Saturation Ratio 0.228
High Saturation Ratio 0.134
Saturation Clustering 0.997
Hue Concentration 0.604
Complementary Balance 0.098
Analogous Dominance 0.788
Temperature Bias 0.617

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). D Major - Research on Harmony - Variation 3 — Catalog raisonné. https://arnaudquercy.art/en/catalogue-raisonne/AQC0654.html

[2] Quercy, A. (2025). Untitled - Gallery. https://artquamanima.com/en/artworks/2024/01/d-major-research-on-harmony-variation-3_7ak.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)

141d5340a630116a18d7ac2ef7f9556b7fe9c4a9cb28fbfb0b9f9e4d2c25e853