Application of an AI-driven visual aesthetic scoring system for style calibration in art works

This research explores AI-driven visual aesthetic scoring systems as tools for evaluating and refining artistic styles, with a particular focus on interior design and computational modelling.The study demonstrates how artificial intelligence can enhance artistic quality and align computer-generated imagery with human aesthetic preferences.By integrating compounded loss functions, curated datasets, and diffusion-based architectures, the model significantly improves visual appeal, stylistic consistency, and task performance.A composite loss-based AI framework was developed using a customised interior design dataset annotated with style tags, aesthetic ratings, and spatial attributes.The system, fine-tuned with user-defined parameters, produced results that were both visually appealing and contextually appropriate.Experimental outcomes revealed statistically robust improvements of 52.54% in portal engagement (Cohen's d = 1.69, p < 0.001, 95% CI: [47.8%, 57.3%]) and 40.08% in agency engagement (d = 1.52, p < 0.001), validated through rigorous statistical testing including permutation tests, bootstrap resampling, and multiple comparison corrections.User studies further indicated that AI-selected or AI-generated images were preferred over other sources, receiving higher aesthetic ratings and engagement levels.