AQC0621

Nanopublication — Computational Image Analysis - AQC0621

by Arnaud Quercy · C Major - Research on Harmony · 2024

Claim 1: Computational Image Analysis - AQC0621

Analysis record [3]: C Major [1] - Research on Harmony (AQC0621) [2] by Arnaud Quercy [2]. Method: k-means. Parameters: 10 colors. Metrics: color distribution, texture, brightness, spatial patterns. Completed: 2026-02-04.

Context

Analysis performed according to MMIDS-CMP-2025 [3] includes four metric categories: (1) Color distribution via k-means (10 colors), (2) Texture analysis using Haralick features, (3) Brightness and contrast measurements, (4) Spatial pattern characterization. Source image [5]: 2372x3558 pixels. Analysis date: 2026-02-04.

Color Analysis

Rank Color Hex % Family Name
1 BB200C 17.6 red-orange firebrick
2 D8B62E 13.4 yellow-orange goldenrod
3 ECD040 13.2 yellow-orange sandybrown
4 DF5B12 12.7 orange chocolate
5 CD3723 10.1 red-orange brown
6 2B241C 9.7 yellow-orange very dark gray
7 ED6B30 9.7 orange tomato
8 7E371A 8.5 orange russet
9 675D58 3.4 orange dimgray
10 E8E0DB 1.7 white gainsboro
11 C7B864 0.3 yellow ochre [Accent]
12 5F2A33 0.3 red russet [Accent]

Color Families:

Family %
yellow-orange 36.3
orange 34.3
red-orange 27.7
white 1.7
yellow 0.3
red 0.3

Accent Colors:

Hex Family Name Chroma
C7B864 yellow ochre 44.4
5F2A33 red russet 25.5

Texture Analysis

Metric Value
Global Roughness 0.211
Mean Local Roughness 0.024
Roughness Uniformity 0.019
Edge Density 0.143
Mean Gradient Magnitude 0.199
Gradient Variance 0.045
Gradient Smoothness 0.0
Directional Coherence 0.009
Pattern Complexity 0.123
Pattern Repetition 1.0
Detail Frequency Ratio 0.625
Spatial Variation 0.165
Texture Consistency 0.484

Brightness & Contrast Analysis

Metric Value
Mean Brightness 0.47
Brightness Variance 0.211
Brightness Uniformity 0.55
Brightness Skewness 0.239
Brightness Entropy 7.55
Rms Contrast 0.211
Michelson Contrast 1.0
Weber Contrast 0.701
Mean Local Contrast 0.026
Contrast Uniformity 0.282
Dynamic Range 1.0
Effective Dynamic Range 0.655
Shadow Percentage 32.949
Midtone Percentage 41.593
Highlight Percentage 25.458
Shadow Clipping 0.012
Highlight Clipping 0.007
Tonal Balance 0.211
Fine Contrast 0.013
Medium Contrast 0.032
Coarse Contrast 0.051
Multiscale Contrast Ratio 0.267
Edge Contrast 0.199
Contrast Clustering 0.516

Spatial Distribution Analysis

Metric Value
Spatial Coherence 0.762
Color Clustering 0.492
Color Transition Smoothness 0.517
Transition Uniformity 0.704
Sharp Transition Ratio 0.1
Transition Directionality 0.011
Mean Saturation 0.753
Saturation Variance 0.056
Low Saturation Ratio 0.103
Medium Saturation Ratio 0.096
High Saturation Ratio 0.801
Saturation Clustering 0.998
Hue Concentration 0.946
Complementary Balance 0.0
Analogous Dominance 0.999
Temperature Bias 0.993

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 Multimodal Institute's computational imaging systems.

References

[1] Arnaud Quercy (2024). C Major - Research on Harmony — Catalog raisonné. https://arnaudquercy.art/en/catalogue-raisonne/AQC0621.html

[2] Quercy, A. (2025). Untitled - Gallery. https://artquamanima.com/en/artworks/2024/01/c-major-research-on-harmony_6xq.html

[3] Quercy, A. (2025). Computational Image Analysis Standard - MMIDS-CMP-2025 https://multimodal.institute/en/publications/2025/10/mmids-cmp-2025-computational-image-analysis-standard-dg1.html

Epistemic profile

Claim typecomputational analysis
Voicethird person
Epistemic statusempirical measurement
Methodologycomputational analysis
Certaintyhigh

Checksum (SHA-256)

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