A novel PTQ-based MPQ framework tailored for SR models is proposed that outperforms existing PTQ-based methods by 1.26 dB in peak signal-to-noise ratio (PSNR) on the Urban100 dataset and introduces a dynamic activation range normalization that alleviates the distributional imbalance caused by the absence of BN.
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.
Hybrid vision backbones such as MambaVision combine convolutional layers, Mamba blocks (based on selective state space models), and self-attention within a single architecture. However, the heterogeneous operator composition of such models poses new challenges for post-training quantization (PTQ): uniform bit-width assignment causes catastrophic accuracy collapse due to widely varying per-block quantization sensitivity. In this work, we present a systematic per-block sensitivity analysis of MambaVision, revealing that mixer blocks (Mamba and attention) are remarkably robust to quantization down to 4-bit weights and 4-bit activations (average accuracy drop of only $\text{0. 0 6 \%})$, while a small number of bottleneck blocks (stem and downsampling layers) are extremely fragile under the same conditions. Based on these findings, we propose a sensitivity-guided mixed-precision assignment strategy that keeps critical blocks at 16-bit floating point, quantizes robust mixer blocks to 4-bit weights and 4-bit activations, and applies 8-bit weights and 8-bit activations to moderately sensitive blocks. Our best configuration achieves 80.66% Top-1 accuracy on ImageNet-1K with GPTQ (-3.29% from 16-bit floating point), recovering +4.68% over uniform 8-bit quantization at a comparable average bit-width of 8.14 bits, while reducing model size by 53.3% and bit operations by 70.8%. We further provide a Hessian mismatch analysis explaining the counterintuitive finding that GPTQ underperforms round-to-nearest under uniform quantization but recovers its advantage under mixed-precision, where 16-bit floating point bottleneck blocks act as error propagation firewalls.
Daisuke Fujita, Ryuto Ishibashi, Ryuto Tanigawa et al.· International Conference on...· 0 citations
Efficient Tuning Before Quantization (ETBQ) is proposed, a pre-conditioning tuning stage for Stochastic Gradient Descent (SGD)-optimized models before PTQ, which improves low-bit PTQ across diverse tasks.
Peng Xia, Junbiao Pang, Muhammad Ayub Sabir· arXiv.org· 0 citations
Across five models from 1B to 32B, SoftWater outperforms the released WaterSIC quantizer at matched head rates on 59 of 60 test points, using none of that pipeline's refinements and cutting head-induced KL by $6.5\times$--$8.3\times at 2 bits.
A cross-modal structural sensitivity asymmetry in VLMs is revealed and SeGO is proposed, a unified structural sensitivity-aware sparse optimization framework that achieves the balance among model parameter amount, quantization accuracy and scaling factors’ search efficiency on InternVL2 and LLaVA series.
Tianqi Zhao, Xinrui Cheng, Yang Su et al.· 0 citations
Across diverse tasks, models, and datasets, HyQuant maintains nearly lossless accuracy with an extremely simple design, demonstrating the efficiency and practical feasibility of hybrid quantization for LLM attention.
Jiarui Ding, Bin Xing, Yu Zhang et al.· 0 citations
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