AQC0690 | NAN-COL000260

Nanopublication — Computational Image Analysis - AQC0690

Ab Major - Research on Harmony - Variation 9

Claim 1: Computational Image Analysis - AQC0690

Analysis record: Ab Major - Research on Harmony - Variation 9 (AQC0690) [1] by Arnaud Quercy [2], per IDS-CMP-2025 [3]. Method: k-means. Parameters: 10 colors. Metrics: color distribution, texture, brightness, spatial patterns. Completed: 2025-10-03.

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: 2093x2617 pixels. Analysis date: 2025-10-03.

A) Color Analysis

Rank Color Hex % Family Name
1 E34914 23.0 orange orangered
2 D97053 14.1 red-orange indianred
3 DE9C77 13.4 orange darksalmon
4 382A24 11.4 orange very dark gray
5 403B49 8.9 violet dusty mauve
6 745D73 8.5 red-violet dusty mauve
7 C92C21 8.0 red-orange firebrick
8 C0A37B 7.8 yellow-orange ochre
9 705247 3.1 orange dimgray
10 833229 1.8 red-orange russet
11 516D86 0.3 blue-violet grayish purple [Accent]
12 967686 0.3 red dusty mauve [Accent]

Color Families:

Family %
orange 50.9
red-orange 23.9
violet 8.9
red-violet 8.5
yellow-orange 7.8
blue-violet 0.3
red 0.3

Accent Colors:

Hex Family Name Chroma
516D86 blue-violet grayish purple 17.3
967686 red dusty mauve 15.5

B) Texture Analysis

Metric Value
Global Roughness 0.16
Mean Local Roughness 0.003
Roughness Uniformity 0.012
Edge Density 0.006
Mean Gradient Magnitude 0.025
Gradient Variance 0.013
Gradient Smoothness 0.0
Directional Coherence 0.503
Pattern Complexity 0.069
Pattern Repetition 1.0
Detail Frequency Ratio 0.62
Spatial Variation 0.112
Texture Consistency 0.523

C) Brightness & Contrast Analysis

Metric Value
Mean Brightness 0.443
Brightness Variance 0.16
Brightness Uniformity 0.64
Brightness Skewness -0.062
Brightness Entropy 6.622
Rms Contrast 0.16
Michelson Contrast 0.983
Weber Contrast 0.712
Mean Local Contrast 0.004
Contrast Uniformity 0.0
Dynamic Range 0.933
Effective Dynamic Range 0.506
Shadow Percentage 22.074
Midtone Percentage 66.483
Highlight Percentage 11.443
Shadow Clipping 0.0
Highlight Clipping 0.0
Tonal Balance 0.0
Fine Contrast 0.002
Medium Contrast 0.005
Coarse Contrast None
Multiscale Contrast Ratio 1.0
Edge Contrast 0.025
Contrast Clustering 0.477

D) Spatial Distribution Analysis

Metric Value
Spatial Coherence 0.782
Color Clustering 0.456
Color Transition Smoothness 0.923
Transition Uniformity 0.913
Sharp Transition Ratio 0.1
Transition Directionality 0.506
Mean Saturation 0.554
Saturation Variance 0.069
Low Saturation Ratio 0.201
Medium Saturation Ratio 0.481
High Saturation Ratio 0.318
Saturation Clustering 1.0
Hue Concentration 0.893
Complementary Balance 0.008
Analogous Dominance 0.921
Temperature Bias 0.949

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. (2024). Ab Major - Research on Harmony - Variation 9 - Catalogue Raisonné. https://arnaudquercy.art/en/catalogue-raisonne/AQC0690.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

Checksum (SHA-256)

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