AI RESEARCH
HyperVQ: Enabling Hyperprior Entropy Modeling for VQ-Based Generative Image Compression
arXiv CS.CV
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ArXi:2512.07192v2 Announce Type: replace Vector Quantization (VQ) based generative image compression has achieved remarkable perceptual quality. However, existing VQ codecs suffer from two fundamental limitations. First, they lack efficient content-adaptive entropy modeling and rely on static frequencies, leading to low coding efficiency. Second, the inherent conflict between discrete indices and continuous priors prevents true end-to-end joint Rate-Distortion (RD) optimization.