AQC0826 | NAN-COL000129

Nanopublication — Computational Image Analysis - AQC0826

E Major - Research on Harmony - Variation 7

Claim 1: Computational Image Analysis - AQC0826

The artwork E Major - Research on Harmony - Variation 7 (AQC0826) [1] by Arnaud Quercy [2] underwent comprehensive computational analysis [3] on 2025-10-03. Method: k-means clustering with 10 colors extracted. Metrics documented: color distribution, texture analysis, brightness/contrast, spatial patterns.

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: 2246x2995 pixels. Analysis date: 2025-10-03.

A) Color Analysis

Rank Color Hex % Family Name
1 C4C6C8 23.6 white silver
2 D5D7D6 18.6 white lightgray
3 C1C96F 9.9 yellow ochre
4 B5C0DE 9.1 blue-violet lightsteelblue
5 CCCE97 8.5 yellow tan
6 A2975E 8.0 yellow ochre
7 B3A781 7.8 yellow ochre
8 9CA8CA 7.3 blue-violet steel gray
9 323020 3.9 yellow darkslategray
10 847B46 3.3 yellow olivedrab
11 545738 0.3 yellow-green dark brown [Accent]
12 414658 0.3 violet dusty mauve [Accent]
13 F0B189 0.3 orange burlywood [Accent]
14 D39384 0.3 red-orange darksalmon [Accent]
15 514C40 0.3 yellow-orange dark brown [Accent]

Color Families:

Family %
white 42.1
yellow 41.5
blue-violet 16.4
yellow-green 0.3
violet 0.3
orange 0.3
red-orange 0.3
yellow-orange 0.3

Accent Colors:

Hex Family Name Chroma
545738 yellow-green dark brown 19.3
414658 violet dusty mauve 11.4
F0B189 orange burlywood 34.1
D39384 red-orange darksalmon 28.4
514C40 yellow-orange dark brown 8.0

B) Texture Analysis

Metric Value
Global Roughness 0.144
Mean Local Roughness 0.02
Roughness Uniformity 0.019
Edge Density 0.1
Mean Gradient Magnitude 0.168
Gradient Variance 0.043
Gradient Smoothness 0.0
Directional Coherence 0.009
Pattern Complexity 0.112
Pattern Repetition 1.0
Detail Frequency Ratio 0.624
Spatial Variation 0.09
Texture Consistency 0.525

C) Brightness & Contrast Analysis

Metric Value
Mean Brightness 0.718
Brightness Variance 0.144
Brightness Uniformity 0.8
Brightness Skewness -2.183
Brightness Entropy 6.682
Rms Contrast 0.144
Michelson Contrast 1.0
Weber Contrast 0.324
Mean Local Contrast 0.022
Contrast Uniformity 0.11
Dynamic Range 1.0
Effective Dynamic Range 0.396
Shadow Percentage 3.961
Midtone Percentage 18.988
Highlight Percentage 77.051
Shadow Clipping 0.002
Highlight Clipping 0.003
Tonal Balance 0.0
Fine Contrast 0.01
Medium Contrast 0.027
Coarse Contrast 0.043
Multiscale Contrast Ratio 0.243
Edge Contrast 0.168
Contrast Clustering 0.475

D) Spatial Distribution Analysis

Metric Value
Spatial Coherence 0.747
Color Clustering 0.657
Color Transition Smoothness 0.587
Transition Uniformity 0.722
Sharp Transition Ratio 0.1
Transition Directionality 0.008
Mean Saturation 0.207
Saturation Variance 0.031
Low Saturation Ratio 0.709
Medium Saturation Ratio 0.288
High Saturation Ratio 0.003
Saturation Clustering 0.999
Hue Concentration 0.638
Complementary Balance 0.175
Analogous Dominance 0.824
Temperature Bias 0.301

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). E Major - Research on Harmony - Variation 7 - Catalogue Raisonné. https://arnaudquercy.art/en/catalogue-raisonne/AQC0826.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/2025/09/ids-cmp-2025-computational-image-analysis-standard-5dq9.html

Epistemic profile

Claim typecomputational analysis
Voicethird person
Epistemic statusempirical measurement
Methodologycomputational analysis
Certaintyhigh

Checksum (SHA-256)

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