AQC0934

Nanopublication — Computational Image Analysis - AQC0934

F Major - Research on Harmony - Variations 13

Claim 1: Computational Image Analysis - AQC0934

Analysis record: F Major - Research on Harmony - Variations 13 (AQC0934) [1] by Arnaud Quercy [2], per IDS-CMP-2025 [3]. Method: k-means. Parameters: 10 colors. Metrics: color distribution, texture, brightness, spatial patterns. Completed: 2026-01-06.

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: 1912x2677 pixels. Analysis date: 2026-01-06.

A) Color Analysis

Rank Color Hex % Family Name
1 EF4C08 19.2 orange orangered
2 E3A963 18.7 orange sandybrown
3 C9B588 14.2 yellow-orange tan
4 BB0D22 11.0 red-orange firebrick
5 231413 10.1 red-orange black
6 F55C1E 9.5 orange chocolate
7 6E4744 6.7 red-orange dark brown
8 EDE8EA 4.2 white white
9 ECBCCA 4.2 red pink
10 A07D58 2.3 orange gray
11 C0CBD9 0.3 blue-violet lightsteelblue [Accent]
12 BDC8D0 0.3 blue silver [Accent]

Color Families:

Family %
orange 49.7
red-orange 27.7
yellow-orange 14.2
white 4.2
red 4.2
blue-violet 0.3
blue 0.3

Accent Colors:

Hex Family Name Chroma
C0CBD9 blue-violet lightsteelblue 8.1
BDC8D0 blue silver 5.4

B) Texture Analysis

Metric Value
Global Roughness 0.226
Mean Local Roughness 0.019
Roughness Uniformity 0.019
Edge Density 0.054
Mean Gradient Magnitude 0.15
Gradient Variance 0.052
Gradient Smoothness 0.0
Directional Coherence 0.002
Pattern Complexity 0.118
Pattern Repetition 1.0
Detail Frequency Ratio 0.612
Spatial Variation 0.114
Texture Consistency 0.672

C) Brightness & Contrast Analysis

Metric Value
Mean Brightness 0.515
Brightness Variance 0.226
Brightness Uniformity 0.561
Brightness Skewness -0.296
Brightness Entropy 7.333
Rms Contrast 0.226
Michelson Contrast 0.992
Weber Contrast 0.745
Mean Local Contrast 0.02
Contrast Uniformity 0.011
Dynamic Range 0.996
Effective Dynamic Range 0.725
Shadow Percentage 24.598
Midtone Percentage 38.586
Highlight Percentage 36.816
Shadow Clipping 0.0
Highlight Clipping 0.001
Tonal Balance 0.0
Fine Contrast 0.01
Medium Contrast 0.025
Coarse Contrast 0.04
Multiscale Contrast Ratio 0.248
Edge Contrast 0.15
Contrast Clustering 0.328

D) Spatial Distribution Analysis

Metric Value
Spatial Coherence 0.782
Color Clustering 0.616
Color Transition Smoothness 0.622
Transition Uniformity 0.636
Sharp Transition Ratio 0.1
Transition Directionality 0.001
Mean Saturation 0.61
Saturation Variance 0.093
Low Saturation Ratio 0.182
Medium Saturation Ratio 0.397
High Saturation Ratio 0.421
Saturation Clustering 0.999
Hue Concentration 0.951
Complementary Balance 0.0
Analogous Dominance 0.987
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. (2025). F Major - Research on Harmony - Variations 13 - Catalogue Raisonné. https://arnaudquercy.art/en/catalogue-raisonne/AQC0934.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

Epistemic profile

Claim typecomputational analysis
Voicethird person
Epistemic statusempirical measurement
Methodologycomputational analysis
Certaintyhigh

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