Why Image Tokenization Needs a Rethink

### The Quality vs. Compression Dilemma

Traditional tokenizers are caught in a tug-of-war, forced to sacrifice image fidelity and fine detail to achieve efficient compression for model training and storage.

### The High Cost of High Resolution

Generating detailed, high-resolution images remains computationally demanding, creating a bottleneck for speed and accessibility.

### Loss of Semantic Coherence

Aggressive compression often strips away context, leading to a loss of semantic meaning and visual coherence in generated outputs.