AQC0958

Nanopublication — Computational Image Analysis - AQC0958

by Arnaud Quercy · G Minor - Research on Harmony - Variations 15 · 2026

Claim 1: Computational Image Analysis - AQC0958

Analysis record [3]: G Minor [1] - Research on Harmony - Variations 15 (AQC0958) [2] by Arnaud Quercy [2]. Method: k-means. Parameters: 10 colors. Metrics: color distribution, texture, brightness, spatial patterns. Completed: 2026-03-05.

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]: 1846x2769 pixels. Analysis date: 2026-03-05.

Color Analysis

Rank Color Hex % Family Name
1 C1C4C7 19.8 white silver
2 DE9B86 14.6 red-orange darksalmon
3 1D1D26 14.5 violet very dark gray
4 453B59 14.0 violet dusty mauve
5 CC8873 12.7 red-orange rosybrown
6 695A6D 6.3 red-violet dusty mauve
7 897A85 5.6 red-violet dusty mauve
8 ECBAA3 5.1 orange burlywood
9 ABA1A8 4.3 red-violet steel gray
10 E5DED9 3.2 white gainsboro

Color Families:

Family %
violet 28.5
red-orange 27.3
white 23.0
red-violet 16.2
orange 5.1

Texture Analysis

Metric Value
Global Roughness 0.247
Mean Local Roughness 0.034
Roughness Uniformity 0.027
Edge Density 0.198
Mean Gradient Magnitude 0.266
Gradient Variance 0.082
Gradient Smoothness 0.0
Directional Coherence 0.014
Pattern Complexity 0.126
Pattern Repetition 1.0
Detail Frequency Ratio 0.669
Spatial Variation 0.194
Texture Consistency 0.419

Brightness & Contrast Analysis

Metric Value
Mean Brightness 0.529
Brightness Variance 0.247
Brightness Uniformity 0.534
Brightness Skewness -0.499
Brightness Entropy 7.527
Rms Contrast 0.247
Michelson Contrast 1.0
Weber Contrast 0.827
Mean Local Contrast 0.036
Contrast Uniformity 0.21
Dynamic Range 1.0
Effective Dynamic Range 0.722
Shadow Percentage 29.162
Midtone Percentage 31.278
Highlight Percentage 39.561
Shadow Clipping 0.0
Highlight Clipping 0.002
Tonal Balance 0.187
Fine Contrast 0.019
Medium Contrast 0.044
Coarse Contrast 0.057
Multiscale Contrast Ratio 0.327
Edge Contrast 0.266
Contrast Clustering 0.581

Spatial Distribution Analysis

Metric Value
Spatial Coherence 0.754
Color Clustering 0.868
Color Transition Smoothness 0.321
Transition Uniformity 0.484
Sharp Transition Ratio 0.1
Transition Directionality 0.012
Mean Saturation 0.249
Saturation Variance 0.024
Low Saturation Ratio 0.524
Medium Saturation Ratio 0.476
High Saturation Ratio 0.0
Saturation Clustering 0.999
Hue Concentration 0.487
Complementary Balance 0.01
Analogous Dominance 0.565
Temperature Bias 0.495

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 (2026). G Minor - Research on Harmony - Variations 15 — Catalog raisonné. https://arnaudquercy.art/en/catalogue-raisonne/AQC0958.html

[2] Quercy, A. (2026). G Minor - Research on Harmony - Variations 15 - Gallery. https://artquamanima.com/en/artworks/2026/03/g-minor-research-on-harmony-variations-15_1yko.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)

6cb82d20ae484032e29bb51db08d4e86e94622653626085cce059ed6da24bbbd