AQC0652

Nanopublication — Computational Image Analysis - AQC0652

Claim 1: Computational Image Analysis - AQC0652

Analysis record [3]: D Major [1] - Research on Harmony - Variation 1 (AQC0652) [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]: 2480x3307 pixels. Analysis date: 2026-02-04.

Color Analysis

Rank Color Hex % Family Name
1 CCBCA2 18.5 yellow-orange tan
2 D1963D 17.0 yellow-orange peru
3 DFAC53 13.0 yellow-orange sandybrown
4 E2D6C7 10.9 yellow-orange lightgray
5 728075 8.6 yellow-green gray
6 2F303C 8.1 blue-violet grayish purple
7 B89C72 7.4 yellow-orange ochre
8 1D1717 7.1 gray black
9 545343 5.9 yellow dark brown
10 A67622 3.6 yellow-orange darkgoldenrod
11 5A3606 0.3 orange russet [Accent]
12 524B6F 0.3 violet dusty mauve [Accent]

Color Families:

Family %
yellow-orange 70.4
yellow-green 8.6
blue-violet 8.1
gray 7.1
yellow 5.9
orange 0.3
violet 0.3

Accent Colors:

Hex Family Name Chroma
5A3606 orange russet 36.4
524B6F violet dusty mauve 22.8

Texture Analysis

Metric Value
Global Roughness 0.224
Mean Local Roughness 0.029
Roughness Uniformity 0.025
Edge Density 0.157
Mean Gradient Magnitude 0.226
Gradient Variance 0.084
Gradient Smoothness 0.0
Directional Coherence 0.009
Pattern Complexity 0.107
Pattern Repetition 1.0
Detail Frequency Ratio 0.631
Spatial Variation 0.134
Texture Consistency 0.569

Brightness & Contrast Analysis

Metric Value
Mean Brightness 0.573
Brightness Variance 0.224
Brightness Uniformity 0.609
Brightness Skewness -0.841
Brightness Entropy 7.529
Rms Contrast 0.224
Michelson Contrast 1.0
Weber Contrast 0.777
Mean Local Contrast 0.032
Contrast Uniformity 0.164
Dynamic Range 1.0
Effective Dynamic Range 0.722
Shadow Percentage 18.787
Midtone Percentage 38.426
Highlight Percentage 42.787
Shadow Clipping 0.035
Highlight Clipping 0.026
Tonal Balance 0.148
Fine Contrast 0.016
Medium Contrast 0.039
Coarse Contrast 0.056
Multiscale Contrast Ratio 0.297
Edge Contrast 0.226
Contrast Clustering 0.431

Spatial Distribution Analysis

Metric Value
Spatial Coherence 0.715
Color Clustering 0.66
Color Transition Smoothness 0.421
Transition Uniformity 0.418
Sharp Transition Ratio 0.1
Transition Directionality 0.012
Mean Saturation 0.384
Saturation Variance 0.066
Low Saturation Ratio 0.488
Medium Saturation Ratio 0.371
High Saturation Ratio 0.141
Saturation Clustering 0.998
Hue Concentration 0.74
Complementary Balance 0.06
Analogous Dominance 0.861
Temperature Bias 0.753

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 1 — Catalog raisonné. https://arnaudquercy.art/en/catalogue-raisonne/AQC0652.html

[2] Quercy, A. (2024). D Major - Research on Harmony - Variation 1 - Gallery. https://artquamanima.com/en/artworks/2024/01/d-major-research-on-harmony-variation-1_79s.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)

d120820d173982d1cb3aaa034025b07ee30732e4de0a3842e3c11666bcd213ca