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

• URL: http://arxiv.org/abs/2608.12239v1
• Date: Wed, 12 Aug 2026 16:41:33 GMT
• Title: HAMP-LIC: Hessian-Aware Mixed-Precision Post-Training Quantization for Learned Image Compression
• Abstract summary: HAMP-LIC is a Hessian-aware mixed-precision post-training quantization framework with a four-stage optimization strategy.<n>It consistently outperforms existing fixed- and mixed-precision PTQ methods across multiple datasets.
• Score: 0.7614628596146601
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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. 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. First, block-wise sensitivity is estimated from the Hessian trace to capture second-order importance. Second, a task-aware refinement module adjusts these sensitivities by jointly considering quantization distortion and rate-distortion performance. Third, guided by the refined sensitivity profile, bit widths are allocated under a global model-size constraint to balance efficiency and reconstruction quality. Finally, block-wise reconstruction using a small calibration set further suppresses quantization error. Experiments on representative LIC models, including Minnen2018 and Cheng2020, demonstrate that HAMP-LIC achieves up to 4.85x model compression with as little as 0.59% BD-rate loss. It consistently outperforms existing fixed- and mixed-precision PTQ methods across multiple datasets while completely eliminating cross-platform encoding-decoding errors.

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