Unified Framework Clarifies Visual Tokenization Quantization Tradeoffs.

A new research paper proposes a unified rate-distortion framework to analyze and compare various discrete visual tokenization techniques, including vector quantization (VQ), product quantization (PQ), and scalar quantization (SQ). Historically, a comprehensive conceptual understanding of the tradeoffs inherent in these methods has been lacking. By treating quantization as a form of lossy compression, the authors characterize the nominal fixed-length coding rate through token count and codebook size, while defining quantization error as distortion. Within this framework, the study addresses three key questions. It theoretically and empirically demonstrates that minimizing distortion, rather than maximizing codebook utilization, is the fundamental objective for achieving high reconstruction fidelity, linking this to gradient discrepancy issues. Furthermore, it establishes two crucial fairness conditions for comparing different quantizers: controlling latent feature statistics and ensuring identical coding rates. Under these controlled conditions, the research successfully recovers the established distortion hierarchy (VQ > PQ > SQ) in modern visual tokenization, confirming that VQ methods generally achieve the lowest distortion.