AI RESEARCH
Differentiable Vector Quantization for Rate-Distortion Optimization of Generative Image Compression
arXiv CS.CV
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ArXi:2604.10546v1 Announce Type: new The rapid growth of visual data under stringent storage and bandwidth constraints makes extremely low-bitrate image compression increasingly important. While Vector Quantization (VQ) offers strong structural fidelity, existing methods lack a principled mechanism for joint rate-distortion (RD) optimization due to the disconnect between representation learning and entropy modeling.