AQC1130

Nanopublication — Computational Image Analysis - AQC1130

Ab7#11 - Research on Harmony - Variations 2

Claim 1: Computational Image Analysis - AQC1130

Analysis record: Ab7#11 - Research on Harmony - Variations 2 (AQC1130) [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: 2421x2243 pixels. Analysis date: 2026-10-02.

A) Color Analysis

Rank Color Hex % Family Name
1 D2C5B2 31.2 yellow-orange silver
2 B3A896 17.4 yellow-orange steel gray
3 5D5447 10.8 yellow-orange dark brown
4 36353A 10.5 gray dusty mauve
5 B92609 9.4 red-orange firebrick
6 151011 5.7 black black
7 BE8063 5.4 orange peru
8 7C7166 4.6 yellow-orange dimgray
9 A0563D 2.8 orange burnt sienna
10 EEE9DC 2.3 yellow white

Color Families:

Family %
yellow-orange 63.9
gray 10.5
red-orange 9.4
orange 8.2
black 5.7
yellow 2.3

B) Texture Analysis

Metric Value
Global Roughness 0.243
Mean Local Roughness 0.021
Roughness Uniformity 0.016
Edge Density 0.078
Mean Gradient Magnitude 0.155
Gradient Variance 0.033
Gradient Smoothness 0.0
Directional Coherence 0.01
Pattern Complexity 0.141
Pattern Repetition 1.0
Detail Frequency Ratio 0.65
Spatial Variation 0.15
Texture Consistency 0.707

C) Brightness & Contrast Analysis

Metric Value
Mean Brightness 0.535
Brightness Variance 0.243
Brightness Uniformity 0.545
Brightness Skewness -0.344
Brightness Entropy 7.427
Rms Contrast 0.243
Michelson Contrast 1.0
Weber Contrast 0.745
Mean Local Contrast 0.022
Contrast Uniformity 0.179
Dynamic Range 1.0
Effective Dynamic Range 0.702
Shadow Percentage 28.171
Midtone Percentage 28.867
Highlight Percentage 42.962
Shadow Clipping 0.009
Highlight Clipping 0.0
Tonal Balance 0.0
Fine Contrast 0.012
Medium Contrast 0.027
Coarse Contrast 0.034
Multiscale Contrast Ratio 0.345
Edge Contrast 0.155
Contrast Clustering 0.293

D) Spatial Distribution Analysis

Metric Value
Spatial Coherence 0.747
Color Clustering 0.768
Color Transition Smoothness 0.606
Transition Uniformity 0.771
Sharp Transition Ratio 0.1
Transition Directionality 0.011
Mean Saturation 0.278
Saturation Variance 0.065
Low Saturation Ratio 0.776
Medium Saturation Ratio 0.116
High Saturation Ratio 0.108
Saturation Clustering 0.999
Hue Concentration 0.903
Complementary Balance 0.027
Analogous Dominance 0.958
Temperature Bias 0.937

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). Ab7#11 - Research on Harmony - Variations 2 - Catalogue Raisonné. https://arnaudquercy.art/en/catalogue-raisonne/AQC1130.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

Documented at

Epistemic profile

Claim typecomputational analysis
Voicethird person
Epistemic statusempirical measurement
Methodologycomputational analysis
Certaintyhigh

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