AQC1116

Nanopublication — Computational Image Analysis - AQC1116

A Minor - Research on Harmony - Variations 14

Claim 1: Computational Image Analysis - AQC1116

Analysis record: A Minor - Research on Harmony - Variations 14 (AQC1116) [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 BEB4A4 22.9 yellow-orange steel gray
2 9A8F7F 17.9 yellow-orange gray
3 ADA292 16.1 yellow-orange steel gray
4 8D796A 9.8 orange grey
5 645D56 9.3 orange dimgray
6 4C443A 8.8 orange darkslategray
7 955F3C 7.1 orange burnt sienna
8 0D0705 4.0 black black
9 DCD8CC 2.4 yellow lightgray
10 7A1A19 1.8 red-orange maroon

Color Families:

Family %
yellow-orange 56.8
orange 35.0
black 4.0
yellow 2.4
red-orange 1.8

B) Texture Analysis

Metric Value
Global Roughness 0.184
Mean Local Roughness 0.022
Roughness Uniformity 0.014
Edge Density 0.079
Mean Gradient Magnitude 0.148
Gradient Variance 0.029
Gradient Smoothness 0.0
Directional Coherence 0.01
Pattern Complexity 0.138
Pattern Repetition 1.0
Detail Frequency Ratio 0.643
Spatial Variation 0.114
Texture Consistency 0.611

C) Brightness & Contrast Analysis

Metric Value
Mean Brightness 0.531
Brightness Variance 0.184
Brightness Uniformity 0.653
Brightness Skewness -0.82
Brightness Entropy 7.269
Rms Contrast 0.184
Michelson Contrast 1.0
Weber Contrast 0.614
Mean Local Contrast 0.022
Contrast Uniformity 0.285
Dynamic Range 1.0
Effective Dynamic Range 0.533
Shadow Percentage 15.792
Midtone Percentage 55.179
Highlight Percentage 29.029
Shadow Clipping 0.007
Highlight Clipping 0.0
Tonal Balance 0.0
Fine Contrast 0.015
Medium Contrast 0.027
Coarse Contrast 0.034
Multiscale Contrast Ratio 0.458
Edge Contrast 0.148
Contrast Clustering 0.389

D) Spatial Distribution Analysis

Metric Value
Spatial Coherence 0.721
Color Clustering 0.844
Color Transition Smoothness 0.623
Transition Uniformity 0.802
Sharp Transition Ratio 0.1
Transition Directionality 0.01
Mean Saturation 0.225
Saturation Variance 0.033
Low Saturation Ratio 0.806
Medium Saturation Ratio 0.162
High Saturation Ratio 0.032
Saturation Clustering 0.999
Hue Concentration 0.969
Complementary Balance 0.0
Analogous Dominance 0.997
Temperature Bias 0.996

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). A Minor - Research on Harmony - Variations 14 - Catalogue Raisonné. https://arnaudquercy.art/en/catalogue-raisonne/AQC1116.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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