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This paper compares four tokenization methods (Affine, AIM, JetFormer, VQ-VAE) for astronomical images within a unified transformer framework, using 640,000 galaxy images to evaluate reconstruction quality, physical property prediction, and morphological preservation. It finds that no single method excels across all tasks, highlighting trade-offs in representation learning.