AQC1104

Nanopublication — Computational Image Analysis - AQC1104

D Minor 7 - Research on Harmony - Variations 1

Claim 1: Computational Image Analysis - AQC1104

Analysis record: D Minor 7 - Research on Harmony - Variations 1 (AQC1104) [1] by Arnaud Quercy [2], per IDS-CMP-2025 [3]. Method: k-means. Parameters: 10 colors. Metrics: color distribution, texture, brightness, spatial patterns. Completed: 2026-10-02.

Context

Analysis performed according to IDS-CMP-2025 [3] includes four metric categories: (a) Color distribution via k-means (10 colors), (b) Texture analysis using Haralick features, (c) Brightness and contrast measurements, (d) Spatial pattern characterization. Source image: 3024x4032 pixels. Analysis date: 2026-10-02.

A) Color Analysis

Rank Color Hex % Family Name
1 B5AC9C 32.8 yellow-orange steel gray
2 A69D90 27.4 yellow-orange rosybrown
3 9A8B75 9.7 yellow-orange gray
4 191511 7.2 black black
5 D8D3C7 6.6 yellow-orange lightgray
6 733D23 4.2 orange russet
7 B04106 3.6 orange russet
8 AA6539 3.5 orange burnt sienna
9 645151 3.1 red-orange dimgray
10 453230 1.9 red-orange darkslategray

Color Families:

Family %
yellow-orange 76.6
orange 11.3
black 7.2
red-orange 4.9

B) Texture Analysis

Metric Value
Global Roughness 0.19
Mean Local Roughness 0.014
Roughness Uniformity 0.009
Edge Density 0.013
Mean Gradient Magnitude 0.093
Gradient Variance 0.011
Gradient Smoothness 0.0
Directional Coherence 0.013
Pattern Complexity 0.138
Pattern Repetition 1.0
Detail Frequency Ratio 0.642
Spatial Variation 0.135
Texture Consistency 0.304

C) Brightness & Contrast Analysis

Metric Value
Mean Brightness 0.565
Brightness Variance 0.19
Brightness Uniformity 0.664
Brightness Skewness -1.248
Brightness Entropy 6.862
Rms Contrast 0.19
Michelson Contrast 1.0
Weber Contrast 0.615
Mean Local Contrast 0.013
Contrast Uniformity 0.27
Dynamic Range 0.98
Effective Dynamic Range 0.71
Shadow Percentage 16.001
Midtone Percentage 51.982
Highlight Percentage 32.017
Shadow Clipping 0.0
Highlight Clipping 0.0
Tonal Balance 0.0
Fine Contrast 0.01
Medium Contrast 0.016
Coarse Contrast 0.021
Multiscale Contrast Ratio 0.466
Edge Contrast 0.093
Contrast Clustering 0.696

D) Spatial Distribution Analysis

Metric Value
Spatial Coherence 0.781
Color Clustering 0.583
Color Transition Smoothness 0.76
Transition Uniformity 0.915
Sharp Transition Ratio 0.1
Transition Directionality 0.014
Mean Saturation 0.228
Saturation Variance 0.047
Low Saturation Ratio 0.834
Medium Saturation Ratio 0.089
High Saturation Ratio 0.078
Saturation Clustering 0.999
Hue Concentration 0.981
Complementary Balance 0.0
Analogous Dominance 1.0
Temperature Bias 1.0

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 Ideamorphic Studies' computational imaging systems.

References

  1. [1] Quercy, A. (2026). D Minor 7 - Research on Harmony - Variations 1 - Catalogue Raisonné. https://arnaudquercy.art/en/catalogue-raisonne/AQC1104.html
  2. [2] Quercy, A. (2025). ORCID https://orcid.org/0009-0000-2662-7790
  3. [3] Quercy, A. (2026). Computational Image Analysis Standard. https://ideamorphism.org/en/measurements/2025/11/ids-cmp-2025-computational-image-analysis-standard-5dq9.html

Where this work lives

Epistemic profile

Claim typecomputational analysis
Voicethird person
Epistemic statusempirical measurement
Methodologycomputational analysis
Certaintyhigh

Checksum (SHA-256)

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