Structural stability and thermodynamics of artistic composition.

Area of Science:

• Digital Image Analysis
• Computational Art
• Image Processing

Background:

• Assessing image clarity is crucial for digital art and scientific analysis.
• Existing metrics may not fully capture structural integrity or color nuances.
• Digital artists often use a "zoom-out" approach to evaluate visual impact.

Purpose of the Study:

• To introduce a new metric for quantifying digital image clarity.
• To demonstrate the metric's versatility across different applications.
• To explore the role of color as a state variable in visual art.

Main Methods:

• A novel metric based on comparing original images with progressively "melted" counterparts (adjacent pixel pair flips).
• Evaluation of structural stability by measuring the presence of stable patterns.
• Testing the metric's robustness against image compression and color quantization.
• Application to textural image analysis, physical systems (Potts model), and digital art color theory.

Main Results:

• The metric assigns zero to uniform/random images and finite values to patterned images.
• The metric respects color diversity and is robust to common image manipulations.
• Demonstrated effectiveness in analyzing textural images and physical system transitions.
• Confirmed color's role as a state variable influencing artistic clarity.

Conclusions:

• The structural stability metric is a versatile tool for extracting nuanced structural information from digital images.
• It enhances decision-making in scientific and creative domains, aiding artists in optimizing designs.
• Combining structural stability with Shannon entropy offers a navigation tool for artistic creation.