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arxiv: 2503.21970 · v3 · submitted 2025-03-27 · 💻 cs.CV

Q-MambaIR: Accurate Quantized Mamba for Efficient Image Restoration

Yujie Chen , Haotong Qin , Zhang Zhang , Michelo Magno , Luca Benini , Yawei Li This is my paper

Pith reviewed 2026-05-22 21:34 UTC · model grok-4.3

classification 💻 cs.CV
keywords Mambaquantizationimage restorationstate-space modelsmodel compressionlow-bit inferenceoutlier handlingedge deployment
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The pith

Q-MambaIR shows that Mamba models for image restoration can be quantized to 2-4 bits with near full accuracy by using a learnable scalar to balance ranges and a flexible allocator to round values.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper tries to establish that state-space models remain effective for image restoration after aggressive quantization once outlier values are handled through two added modules. A sympathetic reader would care because this compression reduces memory use and power draw enough to run the models on edge hardware while preserving the ability to capture long-range dependencies and fine details in restored images. The work focuses on the specific difficulty that extreme activation values create large rounding errors at low bit widths, and it claims the new components fix this without major extra cost during training.

Core claim

Q-MambaIR adds a Statistical Dynamic-balancing Learnable Scalar to adjust the quantization mapping range on the fly and a Range-floating Flexible Allocator that uses an adaptive threshold to round values, thereby reducing peak truncation loss and keeping high-frequency information intact. These changes let the model support pre-deployment weight quantization and deliver higher accuracy than earlier quantized state-space models across image restoration tasks, all while adding only a negligible amount of training computation.

What carries the argument

The DLS and RFA pair that dynamically rescales quantization intervals and applies threshold-based rounding to counteract outlier effects inside Mamba blocks used for image restoration.

If this is right

  • Image restoration accuracy stays close to full-precision levels even when weights and activations use only 2-4 bits.
  • Model storage and memory footprint shrink enough for direct deployment on devices with tight resource limits.
  • Training cost rises only by a small margin compared with the unquantized MambaIR baseline.
  • Weights can be quantized before any device-specific deployment while accuracy is retained.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same range-balancing idea could be tried on other sequence models that suffer outlier problems during quantization for vision tasks.
  • Real-time restoration pipelines on mobile cameras become more feasible once the memory and compute savings are realized in hardware.
  • Extending the allocator to temporal sequences might support quantized video restoration with similar error control.

Load-bearing premise

The two new modules will reliably shrink quantization error from outliers in Mamba features without creating fresh instability or detail loss during training or inference.

What would settle it

Compare PSNR and SSIM scores of Q-MambaIR against prior quantized Mamba baselines on a fixed image restoration benchmark such as GoPro or SIDD at 2-bit width; if the new method shows no clear accuracy gain, the central claim does not hold.

read the original 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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper introduces Q-MambaIR, a quantized Mamba model for image restoration (IR). It proposes two components: the Statistical Dynamic-balancing Learnable Scalar (DLS) to dynamically adjust the quantization mapping range and mitigate peak truncation loss from outliers, and the Range-floating Flexible Allocator (RFA) with an adaptive threshold to flexibly round values while preserving high-frequency details and enabling pre-deployment weight quantization. The manuscript claims that extensive experiments show Q-MambaIR outperforms existing quantized SSMs with SOTA accuracy at 2-4 bits and only negligible increase in training computation and storage savings.

Significance. Should the proposed DLS and RFA components prove effective in reducing quantization errors in Mamba SSMs for IR without introducing instabilities, this work could advance efficient model deployment on edge devices. The focus on ultra-low bit quantization (2-4 bits) addresses a practical challenge in SSM-based vision models. However, without access to the experimental results or derivations, the significance remains potential rather than demonstrated.

major comments (2)
  1. [Abstract] The central claim that 'Q-MambaIR consistently outperforms existing quantized SSMs, achieving much higher state-of-the-art (SOTA) accuracy results' rests on experimental evidence that is not provided in the manuscript. This is load-bearing, as the efficacy of DLS and RFA in mitigating outlier-induced quantization errors cannot be assessed without the promised ablation data or quantitative comparisons.
  2. [Abstract] No equations, pseudocode, or detailed description of how the 'learnable scalar' in DLS is optimized or how the 'adaptive threshold' in RFA is determined are supplied, despite these being the key innovations asserted to solve the performance drop at ultra-low bits.
minor comments (2)
  1. [Abstract] The expansion of 'SSM' as State-Space Models is given, but 'IR' for Image Restoration is not explicitly defined on first use.
  2. [Abstract] The phrase 'much higher state-of-the-art (SOTA) accuracy results' is vague; more precise quantification of the improvements would strengthen the abstract.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback. We agree that the current manuscript (abstract) lacks supporting experimental evidence and methodological details, and we will revise accordingly to strengthen the presentation.

read point-by-point responses

  1. Referee: [Abstract] The central claim that 'Q-MambaIR consistently outperforms existing quantized SSMs, achieving much higher state-of-the-art (SOTA) accuracy results' rests on experimental evidence that is not provided in the manuscript. This is load-bearing, as the efficacy of DLS and RFA in mitigating outlier-induced quantization errors cannot be assessed without the promised ablation data or quantitative comparisons.

    Authors: We agree the abstract alone does not contain the experimental results, ablations, or quantitative comparisons. The full paper includes these, but to address the concern we will revise by incorporating key performance metrics, ablation studies, and direct comparisons to prior quantized SSMs into the abstract or by adding a concise results summary section. revision: yes

  2. Referee: [Abstract] No equations, pseudocode, or detailed description of how the 'learnable scalar' in DLS is optimized or how the 'adaptive threshold' in RFA is determined are supplied, despite these being the key innovations asserted to solve the performance drop at ultra-low bits.

    Authors: We agree that the abstract provides no equations or optimization details for the learnable scalar in DLS or the adaptive threshold in RFA. In the revision we will add the relevant equations, a description of the optimization procedure for the scalar, and the determination of the adaptive threshold, along with pseudocode where space permits. revision: yes

Circularity Check

0 steps flagged

No circularity: abstract supplies no equations or derivations

full rationale

The paper's abstract introduces DLS and RFA as new components and asserts empirical SOTA gains from experiments, but contains zero equations, derivations, fitted parameters presented as predictions, or self-citations. No load-bearing step exists that could reduce by construction to its own inputs, satisfying the requirement to only flag circularity when a specific quoted reduction can be exhibited.

Axiom & Free-Parameter Ledger

1 free parameters · 0 axioms · 0 invented entities

The central claim depends on the effectiveness of two newly introduced components whose parameters are learned from data; no external benchmarks or formal derivations are referenced in the abstract.

free parameters (1)
  • Learnable Scalar in DLS
    Dynamically adjusted during training to set quantization range, therefore fitted to the training distribution.

pith-pipeline@v0.9.0 · 5765 in / 1065 out tokens · 40118 ms · 2026-05-22T21:34:55.226849+00:00 · methodology

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