A Zipfian Analysis of Visual Token Distributions for AI-Generated Images

Andrew Shin

Keywords: Generative AI, Zipf's law

TL;DR: Apply Zipfian analysis to compare visual token statistics of AI-generated images and real images

Abstract: The rapid evolution of text-to-image generation has blurred the perceptual boundary between natural and synthetic imagery. However, it remains questionable whether the statistical structure of generated visual content mirrors the information density of the physical visual world. Drawing upon principles from statistical linguistics, this study investigates the visual language of generative models through the lens of Zipfian dynamics. By analyzing a large-scale corpus of real and synthetic images, we uncover a fundamental divergence between visual syntax and semantics. We find that while generative models have successfully replicated the low-level physics of light, their high-level texture vocabulary exhibits distinct statistical signatures. Our analysis reveals a spectrum of entropy, identifying architectural fingerprints unique to each model. Furthermore, we investigate the relation ship between generated images and prompt complexity, and find that increasing the semantic specificity of text prompts systematically degrades the statistical realism of the generated output.

Submission Number: 5