Hugging Face Trending Papers
Jul 9

LUMI: Tokenizer-Agnostic LLM-Based Lossless Image Compression

Large language model (LLM)-based lossless image compression methods typically represent pixel data through the native text interface of a pretrained model, converting pixel values into token sequences that the LLM processes through its vocabulary head. This design shows that pretrained language models can provide probability estimates for image coding, but it also couples compression to tokenizer behavior, vocabulary-specific numeric tokens, and model-family-specific adaptation.

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
Hugging Face Trending Papers
Aug 12

HAMP-LIC: Hessian-Aware Mixed-Precision Post-Training Quantization for Learned Image Compression

Use this plain-text version for the arXiv abstract field: Learned image compression (LIC) models achieve strong rate-distortion performance but are hindered by high computational complexity and encoding-decoding mismatches across heterogeneous hardware platforms. Uniform fixed-precision quantization alleviates these issues but suffers severe quality degradation at low bit widths because it ignores differences in the quantization sensitivities of individual layers.

arXiv Machine Learning
Aug 11

Statistically-Lossless Quantization of Large Language Models

arXiv:2605. 02404v2 Announce Type: replace Abstract: Model quantization has become essential for efficient large language model deployment, yet existing approaches present clear trade-offs: methods such as GPTQ and AWQ achieve practical compression but are lossy, while lossless techniques preserve fidelity but lack inference acceleration.

By Michael Helcig, Eldar Kurtic, Dan Alistarh