arXiv Computer Vision
FLM: Frequency-Aware Language Models for Generative Image Compression
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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.
arXiv Computer Vision
Oct 1
ResARC: Residual-Aware AutoRegressive Coding for Ultra-Low Bitrate Image Compression
arXiv:2609.39451v1 Announce Type: new Abstract: Progressive autoregressive image codecs provide an appealing paradigm for generative compression by quantizing continuous latents into discrete tokens,...
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
Oct 21
PerQ: Inverse Generative Modeling for Neural Image Compression via Quantization Error Compensation
arXiv:2610.27374v1 Announce Type: new Abstract: Neural image codecs face a fundamental trade-off between pixel fidelity and perceptual quality. Distortion-optimized codecs preserve pixel-wise fidelit...
arXiv AI
Aug 13
HAMP-LIC: Hessian-Aware Mixed-Precision Post-Training Quantization for Learned Image Compression
arXiv:2608. 12239v1 Announce Type: cross Abstract: 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.
arXiv Computer Vision
Oct 16
WaveLLM: Wavelet-Domain LLM Entropy Modeling for Lossless Image Compression
WaveLLM introduces a wavelet-domain entropy model that leverages large language models for lossless image compression. By decomposing images into multi-level wavelet subbands, it transforms heterogeneous prediction tasks into structured, statistically homogeneous subproblems, enabling more effective autoregressive probability estimation. Using a dedicated numerical interface and task-adaptive prompting, WaveLLM, built on Llama‑3.2‑3B with LoRA, achieves state‑of‑the‑art bpsp results on Kodak, CLIC.pro, and DIV2K, outperforming both PixelCNN‑based codecs and recent LLM‑based compressors.