- Updated: August 23, 2026
- 1 min read
HAMP-LIC: Hessian-Aware Mixed-Precision Post-Training Quantization for Learned Image Compression
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. To enable efficient and accurate low‑bit deployment of pretrained LIC models, we propose HAMP‑LIC, a Hessian‑aware mixed‑precision post‑training quantization (PTQ) framework with a four‑stage optimization strategy.
… (full article continues) …
Andrii Bidochko
CTO UBOS
Andrii Bidochko is an AI entrepreneur and researcher focused on AI agents, reinforcement learning, and autonomous systems. He writes about the technologies shaping the future of machine intelligence, from frontier models and agent architectures to real-world AI applications.