arXiv Computer Vision
Sep 4

Tree-Structured Vector Quantization For Efficient And Progressive Image Compression

Tree-VQ introduces a progressive tree‑structured vector quantization framework for learned image compression, organizing discrete codewords in a hierarchical binary tree where each latent token is represented by a routed root‑to‑leaf path. Every prefix of this path yields a valid quantized representation, enabling coarse reconstructions from shallow nodes and successive refinements from deeper nodes. The method incorporates a prefix‑compatible tree entropy model, rate‑aware refinement scheduling, and hierarchical prefix supervision to achieve efficient, low‑latency compression with superior perceptual quality and fewer parameters compared to existing approaches.

By Xinkun Wang, Tianyi Xu, Qingyu Luo, Mingming Ma, Changzhe Jiao, Fu Li, Yi Niu
arXiv Computer Vision
Oct 1

Rethinking Generative Image Compression at Extremely Low Bitrates

The paper introduces RAE-CoD, a diffusion-based compression method that operates in a representation autoencoder space to preserve recognizable content even at extremely low bitrates. It addresses the problem of semantic collapse observed in existing codecs when the bitrate approaches zero, showing that reconstruction losses conflict with semantic objectives and that VAE diffusion models lose efficiency in preserving semantics. Experiments on MSCOCO-30K demonstrate that RAE-CoD outperforms competitors, reducing VFM feature MSE and Fréchet Distance ratios by at least 25.7% and 69.1% at 0.001–0.008 bpp while maintaining stable recognizability and quality.

By Tianyu Zhang, Zhaoyang Jia, Houqiang Li, Dong Liu