AQC0427

Nanopublication — Computational Image Analysis - AQC0427

by Arnaud Quercy · Parisian Thoroughfare · 2022

Claim 1: Computational Image Analysis - AQC0427

Analysis record [3]: Parisian [1] Thoroughfare (AQC0427) [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]: 1536x2048 pixels. Analysis date: 2026-02-04.

Color Analysis

Rank Color Hex % Family Name
1 4F3E2C 15.7 orange dark brown
2 755744 13.4 orange dimgray
3 EAAF93 13.3 orange burlywood
4 99725F 11.3 orange gray
5 302012 10.0 orange very dark gray
6 BD9681 9.0 orange rosybrown
7 B84F30 8.5 red-orange burnt sienna
8 D37559 8.4 red-orange indianred
9 83381F 5.8 red-orange russet
10 F3DEC4 4.6 yellow-orange bisque
11 FCFDF1 0.3 yellow white [Accent]
12 F9F9E8 0.3 yellow-green white [Accent]

Color Families:

Family %
orange 72.6
red-orange 22.8
yellow-orange 4.6
yellow 0.3
yellow-green 0.3

Accent Colors:

Hex Family Name Chroma
FCFDF1 yellow white 6.3
F9F9E8 yellow-green white 8.5

Texture Analysis

Metric Value
Global Roughness 0.211
Mean Local Roughness 0.041
Roughness Uniformity 0.035
Edge Density 0.195
Mean Gradient Magnitude 0.319
Gradient Variance 0.138
Gradient Smoothness 0.0
Directional Coherence 0.024
Pattern Complexity 0.101
Pattern Repetition 1.0
Detail Frequency Ratio 0.649
Spatial Variation 0.075
Texture Consistency 0.864

Brightness & Contrast Analysis

Metric Value
Mean Brightness 0.455
Brightness Variance 0.211
Brightness Uniformity 0.536
Brightness Skewness 0.31
Brightness Entropy 7.704
Rms Contrast 0.211
Michelson Contrast 1.0
Weber Contrast 0.741
Mean Local Contrast 0.045
Contrast Uniformity 0.186
Dynamic Range 1.0
Effective Dynamic Range 0.667
Shadow Percentage 32.135
Midtone Percentage 47.785
Highlight Percentage 20.08
Shadow Clipping 0.005
Highlight Clipping 0.007
Tonal Balance 0.401
Fine Contrast 0.022
Medium Contrast 0.055
Coarse Contrast 0.077
Multiscale Contrast Ratio 0.286
Edge Contrast 0.319
Contrast Clustering 0.136

Spatial Distribution Analysis

Metric Value
Spatial Coherence 0.653
Color Clustering 0.635
Color Transition Smoothness 0.166
Transition Uniformity 0.103
Sharp Transition Ratio 0.1
Transition Directionality 0.027
Mean Saturation 0.468
Saturation Variance 0.038
Low Saturation Ratio 0.201
Medium Saturation Ratio 0.663
High Saturation Ratio 0.136
Saturation Clustering 0.996
Hue Concentration 0.967
Complementary Balance 0.0
Analogous Dominance 0.977
Temperature Bias 0.957

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 (2022). Parisian Thoroughfare — Catalog raisonné. https://arnaudquercy.art/en/catalogue-raisonne/AQC0427.html

[2] Quercy, A. (2025). Untitled - Gallery. https://artquamanima.com/en/artworks/2022/01/parisian-thoroughfare_4ua.html

[3] Quercy, A. (2025). Computational Image Analysis Standard - MMIDS-CMP-2025 https://multimodal.institute/en/publications/2025/11/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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