Q-MambaIR: Accurate Quantized Mamba for Efficient Image Restoration

• URL: http://arxiv.org/abs/2503.21970v2
• Date: Wed, 02 Apr 2025 18:56:09 GMT
• Title: Q-MambaIR: Accurate Quantized Mamba for Efficient Image Restoration
• Authors: Yujie Chen, Haotong Qin, Zhang Zhang, Michelo Magno, Luca Benini, Yawei Li,
• Abstract summary: State-Space Models (SSMs) have attracted considerable attention in Image Restoration (IR)<n>Q-MambaIR is an accurate, efficient, and flexible Quantized Mamba for IR tasks.
• Score: 34.43633070396096
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: State-Space Models (SSMs) have attracted considerable attention in Image Restoration (IR) due to their ability to scale linearly sequence length while effectively capturing long-distance dependencies. However, deploying SSMs to edge devices is challenging due to the constraints in memory, computing capacity, and power consumption, underscoring the need for efficient compression strategies. While low-bit quantization is an efficient model compression strategy for reducing size and accelerating IR tasks, SSM suffers substantial performance drops at ultra-low bit-widths (2-4 bits), primarily due to outliers that exacerbate quantization error. To address this challenge, we propose Q-MambaIR, an accurate, efficient, and flexible Quantized Mamba for IR tasks. Specifically, we introduce a Statistical Dynamic-balancing Learnable Scalar (DLS) to dynamically adjust the quantization mapping range, thereby mitigating the peak truncation loss caused by extreme values. Furthermore, we design a Range-floating Flexible Allocator (RFA) with an adaptive threshold to flexibly round values. This approach preserves high-frequency details and maintains the SSM's feature extraction capability. Notably, RFA also enables pre-deployment weight quantization, striking a balance between computational efficiency and model accuracy. Extensive experiments on IR tasks demonstrate that Q-MambaIR consistently outperforms existing quantized SSMs, achieving much higher state-of-the-art (SOTA) accuracy results with only a negligible increase in training computation and storage saving.

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