QMamba: On First Exploration of Vision Mamba for Image Quality Assessment
Authors: Fengbin Guan
Xin Li
Zihao Yu
Yiting Lu
Zhibo Chen
Venue: ICML 2025 poster (2025)
Keywords: Image Quality Assessment
State Space Model
Prompt Tuning
Primary area: deep_learning->foundation_models

Abstract: In this work, we take the first exploration of the recently popular foundation model, *i.e.,* State Space Model/Mamba, in image quality assessment (IQA), aiming at observing and excavating the perception potential in vision Mamba.  A series of works on Mamba has shown its significant potential in various fields, *e.g.,* segmentation and classification. However, the perception capability of Mamba remains under-explored. Consequently, we propose QMamba by revisiting and adapting the Mamba model for three crucial IQA tasks, *i.e.,* task-specific, universal, and transferable IQA, which reveals its clear advantages over existing foundational models, *e.g.,* Swin Transformer, ViT, and CNNs, in terms of perception and computational cost.  To improve the transferability of QMamba, we propose the StylePrompt tuning paradigm, where lightweight mean and variance prompts are injected to assist task-adaptive transfer learning of pre-trained QMamba for different downstream IQA tasks. Compared with existing prompt tuning strategies, our StylePrompt enables better perceptual transfer with lower computational cost.  Extensive experiments on multiple synthetic, authentic IQA datasets, and cross IQA datasets demonstrate the effectiveness of our proposed QMamba.

QMamba: On First Exploration of Vision Mamba for Image Quality Assessment
Fengbin Guan Xin Li
Zihao Yu
Yiting Lu"
Zhibo Chen'
Abstract
In this work, take the first exploration of the recently popular foundation model, i.e., State Space Model/Mamba, in image quality assess- ment (IQA), aiming at observing and excavating the perception potential in vision Mamba. A se- ries of works on Mamba has shown its significant potential in various fields, egg, segmentation and classification. However, the perception capability of Mamba remains under-explored. Consequently, we propose QMamba by revisiting and adapting the Mamba model for three crucial IQA tasks, i.e. task-specific, universal, and transferable IQA, which reveals its clear advantages over existing foundational models, e.g., Swin Transformer, ViT, and CNNs, in terms of perception and compu- tational cost. To improve the transferability of QMamba, propose the StylePrompt tuning paradigm, where lightweight mean and variance prompts are injected to assist aask-adeetvve trans- fer learning of pre-trained QMamba for different downstream IQA tasks. Compared with existing prompt tuning strategies, our StylePrompt enables better perceptual transfer with lower computa- tional cost. Extensive experiments on multiple synthetic, authentic IQA datasets, and cross IQA datasets demonstrate the effectiveness of our pro- posed QMmmba. The code will be available at:
(a) Cross-Scan
Lbcall--saan
Figure 1. Scanning Methodology Illustration. (a) VMamba's scan- ning method(Liu et al., 2024c) flattens data ID, impairing connectivity by distancing adjacent tokens. (b) Local scanning method scans within across windows, placing semantically similar and distortion-related tokens closer, as shown in the blue boxes.
essary to provide the right optimization direction tailored for image processing techniques, iee, compression (Li et al., 2021; Wu et al., 2021; Yu et al., 2024b), enhancement (Li et al., 2023b; Fei et al., 2023), and ensure the perceptual quality of images. Early works on IQA have been achieved by leveraging the natural scene statisties manner (Mittal al., 201 2012a;b). With the advancements of deep neural networks (DNNs), learning-basee IQA met- rics (Li et al., 2023c; Liu et al., 2022) have demonstrated significant potential for low-level perception, which can be roughly categorized into two types based on the pre-trained backbones: CNN-based and Transformer-based methods.
hand-crafted
Although the impressive progress, learning- based IQA is susceptible to inherent limitations of existing pre-trained backbones: (i) the CNNs are skilled at learning local translation-invriiant features from images while lacking enough long range dependency modeling capability, hin- dering the global quality perception. (ii) The emergence of Vision Transformers presents a great solution to model long- range dependency effectively by leveraging attention mech- anisms. However, the quadratic complexity of self-attention operations poses unaffordable computational costs, espe- cially for large-scale image quality assessment. Recently, an innovative foundation model, the State Space Model, particularly its implementation, i.e., Mamba (Gu & Dao, 2023) has shown considerable potential in various fields for balancing the computational costs and performances,
1. Introduction
Image Quality Assessment (IQA) aims to measure the sub- jective quality of images aligned with human perception, which has been applied in various visual fields, including visual acquisition, transmission, AIGC (Li et al., 2023a; Wang al., 2023a), and UGC creation (Tu al., 2021; Lu al., 2024), etc. Establishing great IQA metric is nec-
'University of Science and Technology of China, Hefei, China. Correspondence to: Xin Li <iin.li@ uste@utte.ce usw..edu.en>..
Proceedings of the ndnd International Conference Machine Learning, Vancouver, Canada. PMLR 267, 2025. Copyright 2025 by the author(s).

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QMamba: On First Exploration of Vision Mamba for Image Quality Assessment
egg., segmentation(Xing et al., 2024; Ma et al., 2024) and classification(Liu et al., 2024c; Zhu et al., 2024). This raises one interesting question, i.e., "whether Mamba can surpass existing backbones on low-level visual perception", which is still under-explored.
model within image quality assessment, and propose the QMamba, powerful IQA metric for three critical tasks of IQA: task-specific, universal, and transferable image quality assessment. This exploration has demon- strated the superior potential of Mamba for subjective perception, advancing the development of IQA.
To answer this question, in this work, we initiate a new ex- ploration of the Mamba model within the field of IQA and introduce the QMamba, a newly developed IQA framework designed to address three key facets of IQA: task task-specific, universal, and transferable image quality assessment. No- tably, since the lack of enough IQA dataset, learning-based based IQA metrics entail the pre-trained backbone for percep- tion knowledge extraction. Meanwhile, VMamba(Liu et al., 2024c), as the most representative framework, has achieved excellent performance on high-level tasks by employing hor- izontal and vertical scanning strategies. However, merely excavating the global perception knowledge is not optimal for IQA, since most artifacts affecting the image quality are related to local textures. Consequently, inspired by Local- Mamba(Huang et al., 2024), we have adopted a local win- dow scanning method, which significantly enhances ability of Mamba to perceive local distortions, thereby demonstrat- ing superior performance. We detail the architecture of QMamba and examine different scanning methods to clarify how the model interacts with and processes image data. Our extensive analysis confirms that QMamba surpasses tradi- tional foundational models, showing superior perceptual accuracy and greater computational efficiency.
To improve the perception transferabilityy of the QMamba, we introduce a simple but effective tuning strategy, i.e. StylePrompt. This strategy enables the ef- ficient knowledge transfer of pre-trained QMamba for downstream IQA tasks, while only tuning fewer learn- able parameters adjust the statistics of perception features.
Extensive experiments have shown that our QMamba has consistently achieved state-of-the-an results on var- ious prominent IQA datasets compared with existing IQA methods, thereby validating the efficacy of the Mamba model in quality assessment. Moreover, our StylePrompt achieves nearly equivalent performance to full model tuning while utilizing only 4% of the whole parameters, demonstrating its effectiveness in perception knowledge transfer scenarios.
2. Related Work
2.1. Blind Image Quality Assessment (BIQA)
Early BIQA methods relied heavily on manually designed features for quality score regression (Mittal et al., 201 2a;b; Venkatanath et al.. 2015; Saad et al., 2012; Min et al., 2018). However, these handcrafted features were insufficient for addressing the complexity of BIQA tasks. With the advent of deep learning, network architectures capable of powerful feature extraction significantly improved quality assessment tasks, with Convolutional Neural Networks (CNNs) and Vision Transformers being the most prevalent.
Moreover, we have explored the perceptual transferabil- ity of Mamba across different datasets, i.e., different con- tents, and degradations. We can find that the Mamba-based IQA metric still suffers from severe performance drop when they encounter large domain shifts between synthetic dis- (Srtions(Sheikh et al., 2006; Lin et al., 2019), authentic Aittortionn(Ghadyrraaa & Bovik, 2015; Hosu et al., 2020), and Artificial Intelligence- Generated eeeerigence-Generaated Content (AIGC)(Li et al., 2023a; Wang et al., 2023a) distortions, etc. To further amplify the transferability of QMamba across range of IQA applications, we introduce simple but effective tuning strategy called StylePrompt. This is based on the finding that the domain shifts in IQA tend to correlate with their feature statistics/style (Lu et al., 2022), such as mean and variance. Concretely, our StylePrompt aims to adaptively adjust the mean and variance of pre-trained QMamba towards the tar- get IQA tasks by setting group of hight-weight learnable 1 xx 1 x C parameters. Extensive experiments have shown that our StylePrompt greatly facilitates the task-adaptive learning of QMamba, enabling efficient knowledge transfer across diverse IQA tasks with fewer parameter costs.
CNN-based BIQA. CNNs have demonstrated robust feature extraction capabilities, leading to their widespread adoption in BIQA tasks. Early works like CNNIQA (Kang al., 2014) used convolutional models for feature learning and quality regression, substantially outperforming handcrafted features. DBCNN (Zhang et al., 2020) introduced dual- stream network to address synthetic and authentic distor- tions separately, integrating these insights for better quality prediction. NIMA (Talebi & Milanfar, 2018) and PQR (Zeng et al., 2017) leveraged pre-trained models on Im- ageNet for quality score prediction, enhancing accuracy through well-established neural architectures. MetalQA (Zhu et al., 2020) employed meta-learning to adapt to un- known distortions by learning shared priors for various dis- tortion types, while HyperIQA (Su et al., 2020) used a hypernetwork to adaptively establish perceptual rules, im- proving generalization. Despite these ad (Saan
The main contributions of this paper are summarized as follows:
We embark on a novel exploration of the Mamba
2

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QMamba: On First Exploration of Vision Mamba for Image Quality Assessment
al., 2023; Zhao al., 2023; Chen et al., 2024a), CNNs' local bias limits their ability to fully exploit both global and local information in BIQA tasks.
2024; Zhang et al., 2024; Liu et al., 2024b), and biomedical image segmentation (Ma al., 2024; Xing al., 2024; Liu et al., 2024a). These studies have demonstrated the effec- tiveness of state space models in visual tasks, providing a solid foundation for further exploration of their potential in visual perception.
Transformer-basedd BIQA. Transformers offer superior global modeling capabilities compared to CNNs. TReS (Golestaneh et al., 2022) addressed CNNs' local bias by cap- turing local structural information with CNNs and then us- ing Transformers for sequential feature extraction. MUSIQ (Ke et al., 2021) designed a multi-scale image Transformer architecture capable of handling images with varying sizes and aspect ratios. DEIQT (Qin et al., 2023) leveraged a Transformer-based BIQA architecture with attention mech- anisms to align with human perception, enhancing model performance and reducing prediction uncertainty. However, the quadratic complexity of Transformers presents chal- lenge, highlighting the need for architectures with linear complexity capable of global modeling for BIQA tasks. In addition to the above methods, many recent works (Zhang et al., 2023; Shin et al., 2024; Yu et al., 2024a) have also adopted Transformer- baased designs and achieved competi- tive performance, yet they still face limitations due to high computational complexity.
3. Method
3.1. Exploring Mamba for Perception
3.1.1. OVERALL FRAMEWORK
The overall architecture of our proposed QMamba model is depicted in Figure 2. To address the unique challenges of visual perception tasks, we developed novel architecture that rethinks the design principles of state space models for visual data. The network incorporates a hierarchical residual structure, where convolutional layers enable effec- tive feature extraction while specialized activation layers compute adaptive gating signals. Our architecture orga- nizes the processing into multiple network stages, each com- bining a strategic downsampling layer with our enhanced Mamba-based processing block. This design enables the construction of multi-level representations at varying resolu- tions, facilitating the extraction of richer perceptual features through progressive abstraction.
2.2. State Space Models (SSMs)
State space models, known for their linear complexity in capturing long-range dependencies, have been integrated into deep learning architectures. The Struetured State Space model (S4) (Gu et al., 2021) was pioneer in deep state space modeling for remote dependency modeling. Subse- quent advancements (Gu et al., 2020; Smith et al., 2022; Fu et al., 2022) further propelled the development of state space models. Mamba (Gu& & Dao, 2023), by integrating selection mechanisms and hardware-awarc algorithms, has shown effective long-range modeling capabilities with linear complexity growth.
To systematically investigate the relationship between model capacity and quality perception performance, we developed three distinct variants of our architecture: QMamba- Tiny, QMamba-Small and QMamba-Base Each variant main- tains the core architectural principles while scaling in com- plexity, allowing us to explore the trade-offs between compu- tational efficiency and perceptual accuracy across different application scenarios.
1.2.2. PERCEPTION WITH LOCAL SCANNING
Initially focused on NLP tasks, Mamba has rapidly ex- panded into other domains. Vim (Zhu et al., 2024) intro- duced a bidirectional SSM block for visual representation learning, achieving performance comparable to ViT (Doso- vitskiy et al., 2020). VMamba (Liu al., 2024c) introduced cross-scan module to traverse spatial domains and con- vert non-causal visual images into ordered block sequences, maintaining linear complexity while retaining global recep- tive fields. LocalMamba (Huang et al., 2024) employed a window-based scanning approach to integrate local in- ductive biases, enhancing the visual Mamba model. These advancements validate the efficacy of Mamba in visual tasks, leading to its application in image classification (Liu al., 2024c; Zhu et al., 2024; Patro& & Agneeswaran, 2024), video understanding (Wang et al., 2023b; Chen et al., 2024b; Li et al., 2024), image restoration (Guo et al., 2024; Shi et al., 2024: Zhen et al., 2024), point cloud analysis (Liang et al.,
While the original Mamba model excels in natural language processing tasks with inherently causal inputs, it faces chal- lenges when applied to visual tasks due to the absence of spa- tial causality. In particular, struggles to capture complex spatial dependencies among image pixels, which hinders its ability to model local distortions effectively.
To address this, VMmmba (Liu et al., 2024c) introduces a bidirectional horizontal and vertical scanning strategy to convert 2D images into 1D sequences suitable for sequential modeling. Although this enables global pixel-level mod- eling, it disrupts the continuity of locally adjacent tokens, weakening the model's ability to perceive fine-grained dis- tortions an essential factor for image quality assessment (IQA).
Inspired by LocalMamba (Huang et al., 2024), we adopt a window-based scanning approach that performs horizon-
3

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QMamba: On First Exploration of Vision Mamba for Image Quality Assessment
QMamba Block 4
Mamba Block lz
QMamba Block L
Mamba Block 4g
Lincar
Style Prompt Injector Pı
Style Promupt Injoctor P₂
A Style Proempt Injector P3
N Style Prompt Injector Ps
LN
Channel-Srale
LN
Channel-Shitt
Quality Awareness Head
SS2D
F
Channel-wise Multiplication
Silu
Channel-wise
Linear
SS2D Block
DWCCorv
Addition
Pooling
Act
Frozen
Norm
Lincar
Trainable
Procppt Companents
Prompt
Style Prompt
Figure Architectural Overview of Qammba Framework and the Detailed StylePrompt Tuning Mechanism.
tal scans within local windows, followed by window-level seans, and applies the same strategy vertically. This method enhances local distortion perception while retaining global awareness through hierarchical composition. Despite down- sampling and aggregation in the input, this scanning scheme maintains balance between local detail and broader con- text.
ples to form cluster structures in feature space while ampli- fying inter-class discriminability. Such feature refinement strategy not only retains critical discriminative information for quality assessment but also significantly improves model sensitivity quality degradation cues, thereby strengthen- ing prediction robustness.
3.2. Tuning the Mamba with StylePrompt
Unlike Lomaaa which relies on attention-based dy- namic routing and suffers from unstable inference and high computational cost, LQMamba adopts hierarchical archi- tecture with fixed-size windows that change with network depth. As illustrated in Figure (b), this structure enables the model to capture multi-scale perceptual cues, from fine- grained distortions to broader contextual patterns, while maintaining stable and efficient inference. It achieves a good balance between accuracy and efficiency, making it well-suited for IQA tasks that demand consistent perception across various distortion types and spatial scales.
Although the Mamba architecture reduces computational complexity compared to other models, achieving higher per- formance still requires substantial number of parameters, which poses challenge for efficient transferable learning, crucial for IQA tasks. We observe that domain shifts in IQA tasks tend to correlate with their feature statistics or style (Lu et al., 2022). such as mean and variance. Building this insight, we propose lightweight tuning strategy, StylePrompt, designed to adjust the mean and variance of the pre-trained QMamba features, thereby aligning them with the distortions and content types of the target domain. This approach enables us to achieve results comparable to full aall-parameter fine-tuning while using a minimal number of parameters, significantly enhancing both the efficiency and performance of QMamba in transferable IQA tasks. Figure 2 illustrates the StylePrompt, which consists of two components that will be described in detail below:
3.1.3. ANALYSIS OF PERCEPTUAL CAPABILITY IN STATE SPACE MODELS
To investigate the feature selection mechanism of State Space Models (i.e..Mamba) in visual quality assessment tasks, we employ t-SNE visualization for deep-layer feature analysis, as illustrated in Figure Visualization results re- veal that Mamba exhibits distinctive characteristic evolution patterns through dynamic state updating mechanisms, pro- gressively enhancing distortion semantic perception across network hierarchies. Specifically, shallow layers preserve di- verse features of original visual signals, while deeper layers adaptively filter redundant background information through gated state selection, intensifying distortion-tyye--peeer specific feature focusing. This drives cettortiionoomggeeeeee sam-
3.2.1. STYLEPROMPT GENERATION (SPG)
We designed the StylePrompt Generation phase to facili- tate the creation of prompts and their interaction with the original features. In a multi-stage network architecture, as images progress through each stage the network, we leam set of prompts P, E R NxtxixC containing N prompt com-

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QMamba: On First Exploration of Vision Mamba for Image Quality Assessment
Blur
Color distortion
Compression
Brightnees
Spatiall distortion
Contrast change
Layer
Layer
Layer
Layer
ResNet QMamba
-26
-10
-2
30
VIT
-3
-50
16
70
Swin Transtormer
27
Figure 3. L-SNE Visualization of Spetofiionn-Seccifcc Feature Separation: QMamba Conventional Backbones
ponents designed to generate affine parameters for style adaptation. These prompts specifically utilized in- ject the distortion information of the current data into the features F; E TR x facilitating learning of the style pertinent to the target domain.
this style information is injected into the original features Fü E B xÕ produced by the current layer. To achieve this, Py is matched to the channel dimensions the cur- rent features using linear layers designed for dimensional alignment. The processed prompt then generates affine pa- rameters "e E R IxIx and 3, E R 1xxIx which modulate the mean and variance of the original feature distribution solely along the channel dimension. This adaptive adjust- ment ensures that the feature distribution is tailored the distortion style of the target domain with minimal computa- tional overhead.
To enable the prompt components to extract specific style information from the input stream dynamically, we pre- dict the weights for different prompt components based on the input features. This process involves performing a global pooling on the current layer's features followed by applying a softmax function obtain the weights for the prompt group. These weights are then applied to the multiple prompt components amalgamate them into new prompt Py. effectively encapsulating the current style information. The operation can be briefly summarized by the following formula:
The process of our StylePrompt Injection can be summa- rized as follows:
% Linear,(Convv (P₃))
(2)
N
8. Linear,(Conv/) (Py))
(3)
Pj
W. Ps
W. Softmax CeltmaxxConvlll 1x (GAP((F)))
(L)
F' F. (1 (1) + 8
(4)
3.2.2. STYLEPROMPT INJECTION (SPI)
Specifically, F` represents the original features after the injection of style information. These enhanced features will serve as the new input to the subsequent layer of the network.
In the SPG phase, the fused prompt Py is created, con- taining the distortion style information of the target do- main. During the subsequent StylePrompt Injection process,
5

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QMamba: On First Exploration of Vision Mamba for Image Quality Assessment
LIVE
CSIQ
TID2013
KADID
LIVEC
KonlQ
LIVEFB
SPAQ
Method
GFLOPS PLCC
SRCC PLCC SRCC PLCC SRCC PLCC
SRCC
PLCC SRCC
PLCC SRCC PLCC SRCC PLCC
SRCC
Average
ILNIQE
0.906
0.902 0,865
0.822 0,648
0.521
0,558
0,534
0.508
0,508
0.537 0.523 0.332
0.294
0.712 0.713
0.618
BRISQUE
0.944
0.929 0.748 0.812 0.571
0.626
0,567
0,528
0.629
0.629
0.685
0.681 0.341
0,303
0.817
0.809
0.664
WaDIQuM($.24M)
2.43G
0.955
0.960 0.844
0.852
0.855
0.835
0.752
0.739
0.671
0.682
0.807
0.804
0.467
0.455
0.763
(BCNN(15.31M)
16.51G
0.971
0.968 0.959
0.946
0.865
0.816
0.856
0.851
0.869
0.851
0.884
0.875
0.551
0,545
0.915
0.911
0.852
TIQA(23.8MM)
0.965
0.949
0.838
0.825
0.858
0.846
0.855
0.850
0.861
0,845
0,903
0.892
0.581
0.541
0.829
MeIaIQA(13.24M0)
1.82G
0.959
0.960
0,908
0.899
0.868
0.856
0.775
0.762
0.802
0.835
0.856
0.887
0.507
0.540
0.815
HyperQQA(27.98M)
4.31G
0.966
0.962
0.942
0.923
0.858
0.840
0.845
0.852
0.882
0.859
0,917
0.906
0.602
0.544
0,915
0.911
0.858
TR=S(152.45M)
20.03G
0.968
0.969
0.942
0.922
0.883
0.863
0.858
0.859
0.877
0.846
0,928
0.915
0.625
0.554
0.858
MUSIQ(27.13M)
9.02G
0.911
0.940
0.893
0.871
0.815
0.773
0.872
0.875
0.746
0.702
0.928
0.916
0.661
0.566
0.921
0.918
0.832
DEIQT((4.044)
5.41G
0.982
0.980
0.963
0.946
0.908
0.892
0.887
0.889
0.894
0.875
0.934
0.921
0.663
0.571
0.923
0.919
0.884
LoDa(*)
23,74G
0.979
0.975
0.901
0.869
0.936
0,931
0.899
0.876
0,944
0.932
0.679
0.578
0.928
0.925
0.882
ResNet-50/23.51M)
4.11G
0.879
0,884
0.861
0.841
0.747
0.686
0.784
0.786
0.868
0.831
0,908
0.886
0.313
0.269
0.907
0.907
0.772
ResNec-101(42.5MM
7.83G
0.918
0.921
0.891
0.867
0.779
0.727
0.722
0.719
0.862
0.824
0.918
0.904
0.420
0.347
0.908
0.906
0.790
RecNeet155((8.11))
11,53G
0.926
0.927
0.923
0.899
0.765
0.717
0.764
0.760
0.859
0.816
0.919
0.898
0,433
0.353
0.907
0.907
0.798
(552T(5.52M)
1,26G
0.786
0.792
0.725
0.717
0.728
0.699
0.832
0.836
0.777
0.730
0.852
0.852
0.521
0.461
0.896
0.896
0.756
VIT-S(21.67M)
4.61G
0.900
0.896
0.832
0.815
0.873
0.859
0.893
0.894
0.831
0,799
0.922
0.905
0.539
0.443
0.919
0.917
0.827
ViT-B(85.80M)
17.58G
0.961
0.955
0.924
0.912
0.904
0.905
0.910
0.908
0.875
0.837
0.913
0.895
0.491
0.452
0.914
0.912
0.854
Swin T(27.52M)
4.51G
0.879
0.883
0,865
0.847
0.937
0.925
0.923
0.922
0.880
0.845
0.901
0.476
0.453
0.922
0.919
0.841
Swin-S(48.84MM
8.77G
0,883
0.896
0,884
0.874
0.931
0.918
0.895
0.894
0.907
0.884
0,931
0.914
0.476
0.433
0,918
0.915
0.847
Swin-B(86.74M)
15,47G
0.945
0.948
0.941
0.935
0.942
0.933
0.934
0,932
0.892
0.858
0.945
0.932
0.507
0.471
0.923
0.921
0.872
QMamba-T T (27.99M)
4.47G
0.959
0.959
0.940
0.918
0.951
0.945
0.934
0.930
0.898
0.866
0.941
0.925
0.675
0.581
0.934
0.929
0.893
QMamha S (49.37M)
8.71G
0.962
0.965
0,921
0.903
0.957
0.955
0.934
0.903
0.874
0.930
0.677
0.573
0.932
0.927
0.893
QMambe-B (87,53M)
15,35G
0.960
0.961
0.908
0.889
0.953
0.949
0.935
0.932
0.908
0.876
0,943
0,930
0.675
0,579
0,933
0.929
0.891
L.QMamba-T(29)
4.44G
0.958
0.959
0.935
0.916
0.952
0.950
0.938
0.923
0.903
0.863
0,943
0.928
0.672
0.574
0,933
0.927
0.892
I.Qaamha (5529911M
8.66G
0.962
0.964
0.933 0.914 0.955
0.949
0,941
0.928
0.907
0.882
0.946
0.934
0.676
0.574
0.933
0.929
0.895
LQMamba-B(93.9MM)
15,30G
0.959
0.951
0.915 0.889 0.965 0.964
0.943
0.941
0.913
0.888
0.947
0,933
0.675
0.582
0.934
0.929
0.896
LoDa has a total of 118.23M model parameters and 8.93M trainalle parameters.
Table I. Performance Comparison for Task-Specific IQA. Bold Indicates the Top Two Results.
4. Experiments
4.1.3 EXPERIMENTAL DETAILS
4.1. Experimental Setup
Our experimental methodology closely follows the training strategy outlined in DEIQT(Qin et 2023), where input images are randomly cropped into ten patches, each with resolution of 224x224. We employed three variants of the VMmmba architecture: QMamba-B, QMamba-S, and QMambaT.. Both QMamba-B and QMamba-S feature an encoder with a depth of 15 blocks, with QMamba-BB B incor- porating an embedding dimension of 128, and QMamba-S using an embedding dimension of 96. In contrast, QMamba- T is designed with reduced depth of 4 blocks and an embedding dimension of 96. Training procedures leveraged weights pre-trained on the ImageNet- KK dataset, spanning a total of 9 epochs. Batch sizes were adjusted according to the respective dataset sizes, e.g., 32 for LIVEC and 128 for KonIQ. We used the Adam W optimizer for training, with the learning rate set to 2 x 10-4 and decay factor of 10 applied every 3 epochs. We compared the performance of ResNet(He et al., 2016), ViT(Dosovitskiyy et al., 2020), and Swin Transformer(Liu et al., 2021), all of which were implemented using the official versions and loaded with
1. DATASETS
We conducted foundational experiments on ten popu- lar IQA datasets, which include four synthetic datasets: LIVE(Sheikh al., 2006), CSIQ(Larson & Chandler, 2010), TID2013(Ponomareeko et al., 2015), and KADID(Lin et al., 2019); four authentic datasets: LIVEC(Ghadiyaram & Bovik, 2015), KonIQ(Hosu et al., 2020), LIVEFB Ying et al., 2020), and SPAQ(Fang et al., 2020); and two AIGC datasets: A1GC2023(Wang et al., 2023a) and AGIQA3K(Li et al., 2023a). We will provide a more detailed introduction to these datasets in Appendix.
4. 1.2. EVALUATION CRITERIA
The evaluation metrics employed in our study are the widely utilized Pearson Linear Correlation Coefficient (PLCC) and Spearman's 's Rank Correlation Coefficient (SRCC), both of which range from 0 to 1. Values approaching 1 denote a higher degree of prediction relevance.
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QMamba: On First Exploration of Vision Mamba for Image Quality Assessment
pre-trained weights. To ensure fairness, other experimental settings were kept as consistent as possible. All experiments were conducted using multiple NVIDIA RTX 4090 GPUs.
well across most datasets, confirming its effectiveness in handling general tasks.
4.2.3. ANALYSIS
4.2. A Comparison Between Different IQA Backhones
Our investigation into the efficacy of QMamba for IQA tasks reveals two key insights through analysis of the KA- DID dataset (7 distortion types) and cross-dataset validation. As shown in Figure 3, t-SNE visualization demonstrates superior distortion discrimination of QMamba: 1) Tightly clustered features for each distortion type, 2) Clear separa- tion between dissimilar artifacts, and 3) Minimal inter-class overlap compared to ViT's partial merging and CNN/Swin architectures' significant feature entanglement.
4.2.1. TASK-SPECIFIC IQA
We conducted comprehensive training and testing across the ten datasets previously introduced, drawing analytical comparisons based on results reported by existing methods. For this task, 80% of the images in each dataset were used for training, while the remaining 20% were reserved for test- ing. Given the predominance of BIQA methods targeting synthetic and authentic datasets, Table 1 presents detailed comparative analysis of state-of-the-arr (SOTA) methods and popular architectures such as ResNet, ViT, and Swin Transformer, in comparison to our QMamba architecture. This comparison elucidates performance discrepancies and computational complexities across different parameter con- figurations. The results in the table demonstrate that the LQ- Mamba configuration achieves optimal performance across six datasets. Compared existing IQA methods with simi- lar parameter counts, QMamba-T T exhibits lower GFLOPS and superior performance, confirming the efficiency and reduced computational complexity of the Mamba architec- ture. Comparative results for the two AIGC datasets are documented in the appendix.
This discriminative capability directly impacts practical per- formance. While QMamba demonstrates modest gains on simpler datasets such as LIVE and CSIQ, which contain only 4 to 5 distortion types. it achieves substantial improve- ments on more complex benchmarks like TID2013 and KADID, which include 24 to 25 distortion types. The ar- chitecture based on state-space modeling enables adaptive frequency processing through a selective scanning mecha- nism, dynamically emphasizing inttortion-rrtiice patterns while suppressing irrelevant features. This approach stands in contrast to convolutional networks with fixed receptive fields and Transformers that tend to over-mix local charac- teristics through global attention.
Our findings indicate that QMamba is well-suited for new quality assessment scenarios involving complex and mixed distortions, such as those introduced by neural compression or generative models. In these cases, traditional architec- tures often fail to capture subtle or entangled artifacts, while the selective modeling in QMamba provides better adapt- ability and robustness.
Mixed Training
LIVE KADID & LIVEC KonlQ& AGIQA3K AIGCIQA2023
Method
Parameters
GFLOPS
PLeC_Average
SRCC.Average
R5
23.51M
4.11G
0.878
0.853
VIT-S
21.67M
4.61G
0.891
0.867
Swin-T
27.52M
4.51G
0.900
0.883
DEIQT
24.04M
5,41G
0.895
0.873
LoDa
8.93M*
23.68G
0.876
0.855
4.3. Efficient Transfer Learning for Hamba-Based IQA
QMamba-T
27.99M
4.47G
0.905
0,886
LQMamba-T
29.87M
4,44G
0.909
0.888
In the context of transferable IQA tasks, we conducted domain-specific training using synthetic datasets (LIVE, KADID), authentic datasets (LIVEC, KonlQ), and AIGC datasets (AIGC2023, AGIQA3K). After training in one domain, models were directly transferred and tested on datasets from the other two domains. We employed the StylePrompt technique, as illustrated in Figure 2, where the architecture was kept intact by freezing all model parame- ters and fine-tuning only the StylePrompt module, which in- volved approximately 4% of the total parameter count. This approach achieved performance levels comparable to those obtained through full-parameter training. The outcomes, presented in Table 3, clearly demonstrate the effectiveness and efficiency of the proposed StylePrompt method for trans- ferable IQA tasks. The value of "f Average" represents the mean performance across all domain transfers, with more detailed results provided in the Appendix.
Trainable paramesers.
Table 2. Performance Comparison for Universal IQA.
4.2.2. UNIVERSAL IQA
We evaluated the effectiveness of the QMamba architecture for universal tasks by employing mixed training across six different datasets: two synthetic datasets (LIVE. KADID). two authentic datasets (LIVEC, KonIQ), and two AIGC datasets (AIGC2023, AGIQA3K). For each dataset, 20% of the data was reserved for performance testing. The average results of models with similar scales are presented in Table 2, with detailed results provided in Appendix. These findings highlight strong multi-tasking capabilities of Mamba. Com- pared to several mainstream models, QMamba performed
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QMamba: On First Exploration of Vision Mamba for Image Quality Assessment
Train
KonlQ & LIVEC (Authentie)
KADID & LIVE (Synthetic)
Test
KADID
LIVE
ATGC2023
AGTQA3K
KonTQ
LIVEC
AIGC2023 AGIQA3K
Fine-tuning Method
PLCC
SRCC
PLCC SRCC PLCC SRCC PLCC SRCC
PLCC SRCC
PLCC SRCC PLCC SRCC PLCC SRCC
Average
DEIQT
0.583 0.595 0.558 0.564 0.802 0.794 0.698 0.632
0.518 0.526 0.579 0.532 0.580 0.575 0.512 0.506
0.601
LoDa
0.600 0.600 0.733 0.729 0.808 0.800 0.715 0.650
0,527
0,513 0,558 0.499 0.592 0.586 0.564 0.541
0.622
Without tuning
0.535 0.501 0.726 0.744 0.789 0.784
0.726 0.744
0.553
0.549 0.595 0,550 0.600
0.612 0.675 0.649
0.642
Lin. (R)bo()))
0.606 0.581 0.783 0.811 0.824 0.804
0.769 0.695
0.871
0.848 0.785
0.751 0.803 0.788 0.771 0.723
0.755
Full luning (93.79M)
0.936 0.930 0.941 0,940 0.885 0.863
0,910
0.851
0.943
0.928 0.899 0,873 0,880 0.859 0.910 0.856
0.908
StylePrompt (3.83M)
0.920 0,912 0.949 0,948
0.877 0.854
0.908
0.854
0,932
0.913 0.888
0.866
0.880
0.865
0.906
0.852
0.901
StylePrompt & R
0.921 0.913 0.944 0.945 0.877 0.853 0,906 0.847
0.931
0.912 0.889 0.860 0.876
0.857
0.905
0.849
0.898
Table 3. Performance Comparison for Transferable IQA.
4.4. Ablation Study
4.4.3. 3. DIFFERENT PROMPT TUNING STRATEGIES
1.4.1. DIFFERENT SCANNING METHOD
To validate the effectiveness of the StylePrompt Generation (SPG) process, we conducted tests by directly learning set of affine parameters Y and /3 to modulate the original features, rather than using prompts for learning, similar to the SSF(Lian et al., 2022). Additionally, to assess the effec- tiveness of the StylePrompt interaction method, we explored various interaction strategies during tuning, including con- volutional prompt interaction and cross-attention prompt interaction. The results in Table 4 demonstrate the superior efficiency and performance of StylePrompt.
As shown in the task-specific results (Table 1) and universal evaluation results (Table 2), models adopting local scan- ning consistently outperform those based on cross-scanning strategies. This motivates our exploration of local scanning mechanisms, as employed in LQMamba.
Although the average performance gap between QMamba and LQMamba may appear small, further analysis reveals that LQMamba performs better on most individual datasets. The marginal overall gain is primarily due to relatively lower improvements on simpler datasets such as LIVE and CSIQ, which contain fewer distortion types and less diverse content. In contrast, on more challenging datasets like TID2013 and KADID-IOK, which feature a wide variety of fineigriied distortions, I.Maamba shows clear advantages (e.g.. SRCC on TID2013: 0.964 vs. 0.949; on KADID: 0.941 vs. 0.932). These results highlight the effectiveness of the local scan- ning design in complex, distortion-rich scenarios where precise modeling of local artifacts is critical.
In addition, we conducted several ablation studies related to the design of the prompts, including variations in the number and shape of the prompts. The detailed results of these experiments are provided in Appendix.
4.5. Discussion and Future Work
Looking ahead, the sequential modeling nature and effi- ciency of our SSM-based architecture make a promising candidate for extension to video and audio quality assess- ment. Given the higher temporal complexity in VQA and the inherent sequential structure of audio signals, method of- fers a unified and lightweight foundation for future research across visual and multimodal quality evaluation tasks.
4.4.2. DIFFERENT MODEL SCALE
In our empirical analysis, as documented in Table 1, we discern that while QMamba-Bast exhibits exceptionally robust quality perception capabilities, QMamba- Saann ei- ther matches or exceeds the performance of QMamba- Bass across the majority of the datasets. Although QMamba- Tiny displays modest decline in performance metrics, it still delivers results that are competitive with current SOTA methods, solely utilizing the capabilities of QMamba- Tiny.
5. Conclusion
In this paper, we introduced QMamba, a novel state space framework for image quality assessment that integrates task-specific evaluation, universal perception, and cross- domain transferability. Extensive experiments demonstrate that QMamba consistently outperforms established vision models, achieving significant improvements in accuracy while requiring substantially lower computational resources. The proposed StylePrompt mechanism enables robust cross- domain adaptation through lightweight dynamic feature re- calibration, setting new benchmarks for IQA and demon- strating strong practical potential in real-world applications.
Tuning Strategy
Parameters
PLCC Avg.
SRCC Avg.
SSF Crossattn Prompt
6.1M
0.750
0.735
12.17M
0.806
0.772
Conv Prompt
28.33M
0.883
0.856
StylePromp((ours)
3.83M
0.911
0.890
Table 4. Ablation Study on Different Prompt Tuning Strategies
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QMamba: On First Exploration of Vision Mamba for Image Quality Assessment
Acknowledgement
Gu, A. and Dao, T. Mamba: Linear-time sequence modeling with selective state spaces. arXiv preprint arXiv:2312.00752, 2023.
This work was supported in part by NSFC under Grant 623B2098, 62021001, and 6237 1434, as well as the China Postdoctoral Science Foundation-Anhui Joint Support Pro- gram under Grant Number 2024T017AH.
Gu, A., Dao, T., Ermon, S., Rudra, A., and Ré, C. Hippo: Recurrent memory with optimal polynomial projections. Advances in neural information processing systems, 33: 1474-1487, 2020.
Impact Statement
This paper presents work whose goal is to advance the field of Machine Learning. There are many potential societal consequences of our work, none which we feel must be specifically highlighted here.
Gu, A., Goel, K., and Ré, C. Efficiently modeling long sequences with structured state spaces. arXiv preprint arXiv:2111.00396, 2021.
Guo, H., Li, J., Dai, T., Ouyang, Z., Ren, X., and Xia, S.-T. Mambair: A simple baseline for image restoration with state-space model. arXiv preprint arXiv:2402.16648 2024.
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QMamba: On First Exploration of Vision Mamba for Image Quality Assessment
A. Appendix/ supplemental material
Model
AGIQA3K
AIGCIQA2023
A.I. Detailed Deseription of the Datasets
GFLOPS
PLCC
SRCC
PLCC
SRCC
Average
ResNet-0023351M)
4.11G
0.901
0.840
0.795
0.797
0.833
We evaluate the performance of the proposed method across eight widely recognized BIQA datasets, comprising both synthetie and authentic datasets. The synthetic datasets in- clude LIVE(Sheikh et al., 2006), CSIQ(Larson & Chandler, et al., 2015), and KADID- 10k(Lin et al., 2019). These datasets feature small number of pristine images that are synthetically distorted using var- ious techniques such as JPEG compression and Gaussian blurring. Specifically, LIVE contains 799 images with 5 types of distortion, CSIQ includes 866 images with 6 dis- tortion types, TID2013 comprises 3000 images with 24 distortion types, and KADID-IOK includes 10125 images with 25 distortion types.
(esNes-101(4250MM)
7.83G
0.907
0.847
0.834
0.831
0.855
RosNet-152(58.1500
11.53G
0
0.832
0.841
0.834
0.852
ViI-T(5.52M)
1.266
0.865
0.787
0.760
0.766
0.795
VIT-8(21,67M)
4.61G
0.891
0.819
0.842
0.822
0.844
2010),
(T-B(85.80M))
17.58G
0.897
0.830
0.853
0.835
0.854
Swin- Tit.))(222.5)))
4.51G
0.906
0.847
0.867
0.844
0.866
Swin-S(48.84N
8.77G
0.908
0.849
0.875
0.857
0.872
Swin-B(86.74M)
15:47G
0.909
0.852
0.886
0.863
0.878
QMamba-T (27.99M)
4.47G
0.913
0.858
0.888
0.873
0.883
S (49.37M)
B.71G
0.858
0.589
0.875
0.884
QMamba-B (87.53M)
15.35G
0.914
0.861
0.886
0.868
0.882
L.Mambu-TT/P99 T(29.77M)
4.44G
0.914
0.862
0.884
0.868
0.882
L.QMamba-S(22.999 (152.91M)
8.66G
0.913
0.864
0.888
0.869
0.884
On the other hand, the authentic datasets include LLVEC(Ghadiyaram & Bovik, 2015), KonlQ-10k(Hosu et al., 2020), SPAQ(Fang et al., 2020), and FL.IVE (Ying et al., 2020). LIVEC consists of 1.162 images with diverse authentic distortions captured by mobile devices. KonIQ- 10k is composed of 10,073 images selected from YFCC- 100M, covering wide range of distortions such as bright- ness, colorfulness, contrast, noise, and sharpness. SPAQ contains 1 11,125 images collected by various smartphones, representing large variety of scene categories. FLIVE, the largest ti-the-wild IQA dataset to date, contains 39,810 real-world images with diverse content, sizes, and aspect ratios.
LQMamba-B(995 ((93,79M)
15.30G
0.915
0.858
0,888
0.871
0.883
Table S. Results of AIGC for task-specific IQA on various back- bones
A.2.3. TRANSFERABLE IQA RESULTS TRAINED ON THE AIGC DOMAIN
We provide the detailed results of Transferable IQA trained in the AIGC domain in Table
A.2.4. DETAILED RESULTS OF ABLATION STUDIES
Ablation study different prompt tuning strate- gies.
In response to the rapid development of Al-generated con- tent, we also employed two additional datasets: AIG- (WQn et al., 2023a) and AGIQA3K(Li et al., 2023a). AIGCIQA2023 contains over 2000 images gener- ated by six state-of-the-an text-to-image models, evaluated through subjective experiment quality, authenticity, and correspondence. AGIQA3K consists of 2,982 images from GAN, autoregression, and diffusion-basee models, with annotations for perceptual quality and text-to-image alignment.
We have provided the detailed experimental data for the ablation study on different prompt tuning strategies in Table 8.
Ablation study results for different prompt designs. We investigated the impact of the number of prompt components on performance and found that setting the number to six yields optimal results. Additionally, we explored whether varying the spatial dimensions and sizes of prompts would enhance performance. Our find- ings show that spatial size of (1,1), focusing solely on the channel dimension, offers the best results. The out- comes of these ablation studies across multiple datasets are presented in Tables and 10.
A.2. Additional Experimental Results
1. TASK-SPECIFIC IQA RESULTS ON AIGC DATASETS
We present the test results of various backbones on the AIGC datasets in Table 5, with bold indicating the best results.
A.3. Limitations
A.2.2. UNIVERSAL IQA DETAILED DATA
Q-Mamba heavily relies pre-trained weights from ImageNet-1K,, which may limit its applicability to domains with significantly different data distributions, Future work could explore pre- training on more diverse datasets to im- prove generalization capabilities. Despite the efficiency improvements brought by the StylePrompt, the computa-
We provide the detailed results of Universal IQA in Table 6.
12

---

QMamba: On First Exploration of Vision Mamba for Image Quality Assessment
Train
LIVE KADID & LIVEC KonlQ & AGIQA3K AIGCIQA2023
Test
LIVE
KADID
LIVEC
KonlQ
AGIQA3K
AIGCIQA2023
GFLOPS
PLCC
SRCC
PLCC
SRCC
PLCC
SRCC
PLCC
SRCC
PLCC
SRCC
PLCC
SRCC
Average
ResNet-50
4.11G
0.916
0.908
0.868
0.869
0.885
0.841
0.896
0.869
0.884
0.813
0.821
0.815
0.865
ViT-S
4.61G
0.921
0.918
0.920
0.917
0.883
0.828
0.911
0.892
0.885
0.822
0.825
0.824
0.879
Swin-T
4,51G
0.927
0.927
0.925
0.921
0.889
0.863
0.929
0.918
0.897
0.840
0.834
0.828
0.892
DEIQT
4.68G
0.907
0.906
0.898
0.895
0.896
0.855
0.928
0.906
0.902
0.841
0.839
0.835
0.884
LoDa
23.68G
0.895
0.902
0.907
0.900
0.872
0.842
0.900
0.875
0.874
0.809
0.809
0.801
0.866
QMamba-T
4,47G
0.923
0.923
0.938
0.932
0.898
0.863
0.932
0.917
0.906
0.851
0.835
0.830
0.896
LQMamba-T
4.44G
0.929
0.926
0.943
0.939
0.899
0.863
0.936
0.921
0.908
0.853
0.840
0.828
0.899
Table 6. Performance comparison for universal IQA.
Train
AIGC2023 & AGIQA3K (AIGC)
Test
QonIQ
LIVEC
KADID
LIVE
Fine-tuning Method
PLCC
SRCC
PLCC
SRCC
PLCC
SRCC
PLCC
SRCC
Average
DEIQT
0.578
0.505
0.684
0.638
0.498
0.464
0.752
0.750
0.601
LoDa
0.564
0.500
0.542
0.548
0.555
0.524
0.840
0.833
0.622
Without_tuning
0.636
0.587
0.667
0.637
0.460
0.425
0.815
0.838
0.642
Probe()))
0.818
0.792
0.790
0.761
0.570
0.540
0.799
0.826
0.755
Full_tuning (93.79M)
0.943
0.927
0.910
0.878
0.937
0.932
0.938
0.933
0.908
StylePrompt (3.83M)
0.929
0.909
0.902
0.871
0.918
0.913
0.926
0.922
0.901
StylePrompt& & R
0.929
0.909
0.899
0.859
0.918
0.911 0.926
0.927
0.898
Table 7. Performance comparison for transfcrable IQA (trained on AIGC).
Tinin
Kooli)& & LIVEC:
KADID & LIVE
AIGUZIES & AGIQA3K
Text
KADID
LIVE
AICIC2023 AGIQASK
Kon1Q
LIVEC
ARGC20233 AGIQATK
KonlQ
KADID
LIVE
Style and Prosutt method PLCC SRCC PLCC SRCC PLCC SRCC PLCC PRCC SRCC SRCC PLCC'SOC PLOC SRCC SROC PLCC SRCC PLOC SRCC PLCC SROC
IMF
0,718 0,700 0,735 0.769 0.804 0,801 0,714 0.667 (.863 0,829 0.643 0,598 0.668 0.675 0.714 0.685 0.866 0,828 0,730 0.706 0.724 0,708 0,800 0.856
0.743
Coossum_Promyo(22.. 0.843 0.898 0,897 0.783 0.752 0,821 0,706 0.843 (1816 D1641 0,802 0,782 0,33 0.726 0.842 0.694 0,616 03831 0.894 0.897
0.789
Cany, (2.M)) 0.911 0,910 0.945 0.946 (.89) IJ820 0.800 0.825 (1897 0,581 0.797 0.761 0.864 0.827 0.861 0,878 D.80S
0.884 0.8OS O 0.756 0,501 0.809 0U932
0.033
0.869
StydekhampoooursssssgMM 0,929 0,912 0.949 0.948 0.854 0.908 0.854 0.932 0.913 0,SSS 0,866 0,880 0.865 0.906 0.852 0.929 0.909 0,902 0.871 0.918 0,913 0:5126
0.922
0.901
Table 8. Ablation study on different prompt tuning strategies
tional demands for training and deploying QManba on very large-scale datasets or in real-time applications might still be substantial. Investigating methods to further reduce compu- tational complexity without sacrificing performance could be beneficial. The current study focuses on specific types of distortions, and there may be other types of distortions that have not been sufficiently explored. Expanding the evalu- ation to cover a broader range of distortions could provide a more comprehensive validation of QMamba's 's robustness. Given that synthetic datasets might not fully capture the
complexity and variability of authentic data, there is a risk of overfitting to these synthetic examples. Further evaluations on more diverse and extensive authentic datasets would help ensure the model's robustness and practical applicability. While QMamba shows promising results in cross-domain transferability, its effectiveness across vastly different do- mains (e.g.. medical imaging versus natural images) has not been thoroughly validated. Further studies are needed to test and possibly adapt QMamba for such diverse applications.
13

---

QMamba: On First Exploration of Vision Mamba for Image Quality Assessment
KonlQ
AIGC2023 AGIQA3K
Test
KADID
AIGC2023 AGIQA3K
KoalQ
LIVEC
AIGC2023
AGIQAJK
KoniQ
LIVEC
KADID
LIVE
PLOC SROC PLOC SROC PLCC PLOC PRCO
PLOCPPLCCCSOOO SROC PLOC SRCC PLOC SROC PLOC SRCO SRCC SROC SRCC PI CC SROC PLOC SRCC
Average
N-1
0.914
0,907 0.933 0,944 0.877 0880 0.900 0.833 0.924 0.906 0,873 0.846 0,872 0,850 0.898 0.834
0.924 0,907 0.890 0,855
0,904 0.899
0.940 0.937
0.8928
N-2
0.913
0,905
0.938
0,939 0.874 0881
0.895 0.837
0.930
0.912
0.886
0.853 0.871 0,851
0.892 0,835
0.927
0,905
0.902.
0.865
0.911 0.906
0.926
0.922
0.8936
N-4
0.920
0.913
0.944 0,949 0.875
0881
0.904 0.840
0.929
0.913
0,877 0.849 0,876 0.857
0.904
0.842
0.927
0.904
0,885
0.847 0.913
0:907
0.925
0.931
0.8951
N-6
0.920
0,912
0.949 0.948 0.877 0.844
0.908
0.854
0.932
0.913
0,888 0.866 0.880 0.865
0.906
0,882
0.992
0.909
0.902
0.871 0.918 0.913
0.926
0.922
0.9006
N=8
0.920
0,914
0.930 0.934 0.874
0.851
0.903
0.841
0.930
0.912
0,873
0.843 0.876
0.854
0.899
0.841
0.927
0.906
0.889
0.859 0.918 0.912
0.922
0.926
0.8939
N=10
0.919
0.913
0.930
0.936 0.877 0.856 0.901 0.810
0.929
0.910
0,880 0.849 0.877 0.853 0.900 0.840 0.925 0.906 0.889 0.851 0.918 0.914
0.923
0.924
0.8942
Table 9, Ablation study results for different numbers of prompts (N).
Thin
KonlQ
KADID
AIGC2023 & AGIQA3K
KADID
AIGC2023 AGIQA3K
KoaIQ
LIVEC
AIGC2025
AGIQASK
KoalQ
LIVEC
KADID
(H.W)
PLCC
SRCC
PLCC
PLCC SRCC
Average
(1.1)
(7.7)
(14,14)
0.924 0.917
0.947 0.948 0.876 0.852 0.906 0.844
0.931
0.912
0.881
0,848 0,875 0.857 0.902 0.843
0.928
0.906
0,890 0.855 0.921 0.915
0.927
0.931
0.897
(28.28)
0.917
0.909 0.948 01947 0.871 0.849 0.905 0.847
(3.929
0,9099
0.876 0.845 0.833 0.855 0.903 (1841
0.928
0,905
0.805 0.860 0.902 (1898 0.932
0.931
0.895
layer HWW
0.921 0.914 0.948 0.948 0,580 0.854 0.905 0.844
0.929 0.909 0.876 0.850 0.879 0.859 0.903 0.844 0.928 0.906 0.891 0.858 0.913 0.908 0.920 0.919
0.896
Table 10. Ablation study for different prompt shape
14


Reviews:
[{"review_id": "YumxOb4vgB", "reviewer": "Reviewer_nNBw", "summary": "This paper proposes a no-reference image quality measure, and specifically it is the first work to explore vision mamba for blind IQA. \nExperimental results on task-specific, universal, and transferable IQA tasks demonstrate the advantages of the proposed method.\nThe whole work is interesting and may be useful for the following studies.", "questions": "", "limitations": "", "rating": 4, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "**Q1: About the suggestion to develop an all-in-one model that performs well on all databases.**\n\n**A1:** Thanks for your great questions and impressive suggestions. We have conducted a thorough analysis of the reasons why the differences occur and have raised some proposals for how to design an all-in-one model as follows.\n\nThe differences stem from:\n1. The noise and small size of some datasets, i.e., LIVE, and CSIQ. The large LQMamba-T is susceptible to overfitting risk with the small dataset, thus causing similar or slightly lower performance with smaller LQMamba-T.\n2. The mismatched size between different backbones, e.g., -S, -T, -L, and the dataset cannot excavate the representation capability of IQA, which causes the inconsistency between model size and performance on LIVE and CSIQ.\n\nBased on the above analysis, we believe the essential question for the all-in-one model to perform best on all datasets is \"how to increase the dynamic capability of the Q-Mamba for different datasets.\" Based on the careful survey, we believe the following strategies can achieve an all-in-one Q-Mamba, which will be investigated in our future work.\n\n**(i)** Dataset-Aware Prompt Tuning. We implemented *StylePrompt* for lightweight domain adaptation via feature modulation. We plan to extend this with dataset-specific prompts to activate different perception pathways based on the input domain, allowing dynamic adaptation without modifying backbone weights.\n\n**(ii)** Multi-Domain Joint Training. We are extending our IQA experiments (Tables 2 and  6) with a multi-domain training protocol incorporating domain generalization losses (e.g., feature alignment or contrastive losses) to reduce domain gaps.\n\n**(iii)** Preliminary Unified Model Experiments. In the final version, we plan to add experiments with a single QMamba variant enhanced with dataset prompts, trained jointly on all datasets. Early results (Tables 2 and  6)  across six domains have shown the feasibility for universal deployment.\n\n**Q2: About the suggestion to compare with more representative and recent IQA methods.**\n\n**A2:** We thank the reviewer for the constructive suggestion. We fully agree that incorporating comparisons with both classical and recent SOTA methods such as BMPRI, RichIQA, and MINT-IQA can provide a more comprehensive evaluation.\n\nTo address this, we have collected partial results from these methods on several popular IQA datasets and compared them with our QMamba framework. As shown in the table below, QMamba achieves highly competitive PLCC scores, outperforming or matching recent strong baselines on many benchmarks.\n\n| Method    | TID2013 | CLIVE | KonIQ | SPAQ |\n|-----------|---------|-------|-----------|------|\n| BMPRI     | 0.608   | 0.392 | 0.424     | 0.611|\n| MINT-IQA  | 0.899   | 0.925 | 0.945     | 0.932|\n| RichIQA   | -       | 0.912 | 0.950     | 0.923|\n| Ours      | 0.965   | 0.913 | 0.947     | 0.934|\n\n*Table 1: PLCC comparison with classical and recent IQA methods*\n\n\nIn the final version, we will further extend the comparisons to include more datasets if available, ensuring a thorough and fair benchmarking.\n\n**Q3: Some discussions on the generalization to video and audio-visual quality assessment.**\n\n**A3:** We appreciate the reviewer’s forward-looking suggestion. Indeed, exploring the extension of our proposed architecture to video and audio-visual quality assessment is a promising direction, and it is part of our planned future work.\n\nTo provide an initial insight, we conducted preliminary experiments by adapting QMamba to the video domain. As shown in the table below, our QMamba (Video) achieves performance comparable to FastVQA while consuming fewer GFLOPs:\n\n| Model                        | GFLOPs  | PLCC  | SRCC  |\n|------------------------------|---------|-------|-------|\n| FastVQA (27.70M)             | 279.1G  | 0.876 | 0.877 |\n| LQMamba (Video, 27.99M)      | 239.3G  | 0.879 | 0.876 |\n\n*Table 2: Preliminary results for video quality assessment.*\n\nVideo quality assessment (VQA) typically demands much higher computational resources due to temporal modeling. Our efficient SSM-based architecture, originally designed for image quality perception, offers a solid foundation to balance performance and computational cost in VQA tasks.\n\nMoreover, since audio signals inherently possess sequential structures, we believe the state space modeling capability of our architecture is well-suited for audio or audio-visual quality assessment. We envision that our work can serve as a strong baseline for future research on applying selective state space models in both video and audio domains.\n\n**Q4: About the suggestion to include more surveys on image and video quality assessment.**\n\n**A4:** We appreciate the reviewer’s suggestion. In the final version, we will include a more comprehensive survey of image and video quality assessment literature and explore recent works that can be meaningfully integrated with our proposed framework.", "claims_and_evidence": "The claims are well supported by the experimental validations.", "methods_and_evaluation_criteria": "The paper follows the common evaluation procedures for IQA methods as frequently used in this area.", "theoretical_claims": "Not involved.", "experimental_designs_or_analyses": "The experimental validation is well-conducted, which follows the common procedures.", "relation_to_broader_scientific_literature": "This mamba-based image evaluation model has some potential impact on other fields. For example, it can be used as a reward model when improving the perceptual quality of image processing systems.", "essential_references_not_discussed": "There are no important references that are not discussed.", "other_strengths_and_weaknesses": "Some comments especially regarding to the weaknesses are as follows:\n1. The authors introduce  a new mamba-based framework and many methods based on this framework (with different backbones). Different methods with different backbones show advantages on different databases. It would be better if an all-in-one model can work well on all databases.\n2. More experimental validations are suggested to be given.  More state-of-the-art methods are suggested to be compared, for example traditional hand-crafted method BMPRI, more recent CvT-based method RichIQA, and the latest LMM-based method MINT-IQA.\n3. The authors may give some discussions on whether the introduced methodology can be generalized for video quality assessment and even audio-visual quality assessment.\n4. Some surveys for image and video quality assessment are suggested to be given for better referring of the related topics.", "other_comments_or_suggestions": "More intuitive visualizations are suggested to be given in the paper, especially in the experimental validation part. Only 3 figures are given in the paper.", "questions_for_authors": "See the above comments.", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "cRRUz6CccP", "reviewer": "Reviewer_qazE", "summary": "In this paper, an algorithm named QMamba is proposed for NR-IQA. QMamba is based on Mamba, but it employs style prompt tuning method to boost its performances with small learnable parameters. Specifically, style prompt tuning consists of two steps: SPG and SPI. In SPG, it generates the style prompt from input features by using GAP and 1x1 convolution. Then, from the generated style prompt, it predicts the affine parameters to adjust input features. Experimental results on various IQA benchmarks show that the proposed algorithm achieves better performances than existing methods.", "questions": "", "limitations": "", "rating": 4, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "**Q1: About the reason for relatively lower performance on LIVE and CSIQ Datasets**\n\n\n**A1:** Thanks for your positive and constructive comments. We will provide a more thorough explanation for this result in the revision from two perspectives:\n\n**(i) Limited Dataset Scale and Diversity in LIVE and CSIQ datasets.** LIVE and CSIQ are early synthetic IQA benchmarks with limited image counts (i.e., 799 and 866 respectively) and fewer distortion types. These lead to constrained data diversity in contrast to other datasets, such as TID2013 and KADID-10k, which include over 3,000 and 10,000 images with 24–25 distortion types, providing a broader distortion spectrum.\n   \n**(ii) Mismatched Size between Model and Dataset.** Notably, the scale-up of model size is usually required to be consistent with the scale of datasets. The performance of large-scale models in IQA tasks often relies on training with diverse and large-scale datasets to fully activate their capabilities. As shown in Table 1 of our manuscript, lightweight IQA models (e.g., DBCNN) tend to perform well on smaller datasets such as CSIQ, but their effectiveness significantly drops on real-world datasets like LIVEC and KonIQ, as well as large-scale synthetic datasets such as TID2013 and KADID. In contrast, recent large-scale models, e.g., ResNet152, Swin-B, ViT-B, and our Q-Mamba, have demonstrated superior performance on more complex real-world and large-scale synthetic datasets, while still maintaining acceptable results on smaller ones. However, these models achieve relatively lower performance on LIVE and CSIQ due to the insufficient dataset diversity and overfitting risks.\n\n**Q2: About the inconsistency between model size and performance on relatively small datasets, i.e., LIVE and CSIQ.**\n\n**A2:** As stated in response A1, the mismatch between model size and dataset scale can hinder the ability of an IQA model to fully demonstrate its potential, thereby preventing it from achieving optimal performance. On small datasets, the observed inconsistency between model size and performance can be attributed to two main factors:\n\n**(i)** The overfitting risk, where the large model tends to memorize the limited patterns in small IQA datasets, resulting in poor generalization capability to unseen testing data.\n\n**(ii)** Dataset bias.It is common sense that small IQA datasets often contain significant subjective bias in human-provided scores. This bias is especially impactful for large models, leading to unstable training and inconsistent performance.\n\n**Q3: About inference speed comparison.**\n\n**A3:** We sincerely thank you for highlighting this important point. As model efficiency is a central goal of QMamba’s design, we conducted a comprehensive inference speed comparison to better support our claims. We randomly sampled a total of 20,000 images across multiple IQA datasets, including synthetic and authentic distortion types, and evaluated the inference latency of three representative models of similar scale: QMamba-Tiny, ViT-Small, and Swin-Tiny. The results are summarized below:\n\n| Model      | Params / GFLOPs   | Total Time (s) | Time / Image(s) |\n|------------|-------------------|----------------|-----------------|\n| ViT-S      | 21.67M / 4.61G    | 226.42         | 0.0113          |\n| Swin-T     | 27.52M / 4.51G    | 363.47         | 0.0182          |\n| QMamba-T   | 27.99M / 4.47G    | 211.95         | 0.0106          |\n\n*Table 1: Comparison of inference efficiency among QMamba-Tiny and popular backbones.*\n\nAs shown, QMamba-Tiny achieves the lowest average inference time per image, while maintaining comparable model size and computational complexity. This confirms the practical efficiency of our method in real deployment scenarios and complements the theoretical analysis in the main paper. We appreciate the reviewer’s suggestion and will incorporate these results into the final version to more fully demonstrate QMamba’s efficiency advantage.\n\n**Q4: About the comparison of some suggested methods**\n\n**A4:** Thank you for your suggestions. These are very creative methods, and I will incorporate them into the final version of the comparison. Since they only report results on a few datasets in the paper, I will briefly present part of the comparison results here.\n| Method  | CLIVE      |            | KonIQ  |            | SPAQ      |            |\n|---------|------------|------------|------------|------------|----------|------------|\n|         | PLCC       | SRCC       | PLCC       | SRCC       | PLCC     | SRCC       |\n| GRepQD  | -          | 0.822      | -          | 0.855      | -        | -          |\n| QCN     | 0.893      | 0.875      | 0.945      | **0.934**      | 0.928    | 0.923      |\n| Ours  | **0.913**      |**0.888**   | **0.947**      | 0.933      | **0.934**    | **0.929**      |", "claims_and_evidence": "Yes, the claims are supported by experimental results including extensive ablation studies.", "methods_and_evaluation_criteria": "Yes, the proposed algorithm technically sounds and the evaluation process seems fair.", "theoretical_claims": "This paper does not propose any theoretical claim.", "experimental_designs_or_analyses": "Yes, this paper follows the standard evaluation protocol in this field.", "relation_to_broader_scientific_literature": "Recently, state space models are applied to various deep learning tasks such as image classification, video understanding, image segmentation and point cloud analysis. However, in IQA, SSM approach has been under researched. This paper applies SSM approach to IQA tasks and proposes a simple but effective algorithm.", "essential_references_not_discussed": "Some recent papers are not addressed and compared. It would be better to compare with these algorithms as well. \n\n- [1] Learning generalizable perceptual representations for data-efficient no-reference image quality assessment. WACV24 \n- [2] Blind image quality assessment based on geometric order learning. CVPR24", "other_strengths_and_weaknesses": "Please find weaknesses in questions for authors section.", "other_comments_or_suggestions": "N/A", "questions_for_authors": "In overall, I think that the proposed algorithm has meaningful results and enough technical contribution. I only have a few concerns as below:\n\n- The proposed QMamba achieves better performances than conventional algorithms in overall. However, it shows relatively low performances on the LIVE and CSIQ datasets. It would be helpful to have an explanation of the reasons behind these results.\n\n- Also, for the relatively small sized datasets, such as LIVE and CSIQ, QMamba tends to show lower performance as the model size increases. Is it because of over-fitting?\n\n- The efficiency of the model is key contribution of the proposed QMamba. Therefore, it would be good to have inference speed comparison as well.", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {"ethical_review_concerns": "N/A"}}, {"review_id": "daIakXAxYw", "reviewer": "Reviewer_Gas2", "summary": "This paper introduces QMamba and LQMamba, a new network architecture based on Mamba for image quality assessment.\nQMamba operates through a global scanning approach while LQMamba operates through a local scanning approach.\nIn addition, a style prompt injector is proposed to adjust mean and variance of the feature so this enables easy adaptation to downstream IQA tasks.\nBoth QMamba and LQMamba achieves SOTA performance in experiments.", "questions": "", "limitations": "", "rating": 4, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "**Q1: Key difference between LQMamba and LocalMamba.**\n\n**A1:** Thank you for pointing this out. We want to clarify that while **LQMamba** is inspired by **LocalMamba**, it differs in *design motivation* and *technical implementation*, specifically with a **hierarchical structure** for image quality assessment (IQA).\n\nLocalMamba uses *adaptive scan selection* with multiple window configurations via attention-based routing. While suitable for high-level classification tasks, this introduces *unstable inference behavior* and higher *computational cost*, especially in IQA, where local distortions dominate.\n\nIn contrast, **LQMamba adopts a fully hierarchical design** for both architecture and scanning strategy. Each layer processes visual tokens within a *fixed-size local window*, with the window size *progressively varying with network depth*. This enables the model to:\n\n- Capture multi-scale perceptual cues, from fine-grained distortions in early layers to broader context in deeper layers;\n- Preserve stability by avoiding dynamic path selection;\n- Reduce computational overhead while achieving strong generalization.\n\nThis design reflects our *task-specific and universal motivation*: IQA requires consistent perception across varying distortion types and scales, and hierarchical processing is ideal.\n\nTo validate the effectiveness of our structure, we compared **LQMamba-T** and **LocalMamba-T** across four IQA benchmarks:\n\n| Dataset | LocalMamba-T   | LQMamba-T      |\n|---------|----------------|----------------|\n| LIVEC   | 0.843 / 0.791  | 0.903 / 0.863  |\n| KADID   | 0.861 / 0.870  | 0.938 / 0.923  |\n| KonIQ   | 0.900 / 0.890  | 0.943 / 0.928  |\n| SPAQ    | 0.882 / 0.881  | 0.933 / 0.927  |\n\n*Table 1. Performance comparison between LocalMamba-T and LQMamba-T on four IQA benchmarks.*\n\nThese results show that **LQMamba consistently outperforms LocalMamba**, especially on authentic and distortion-diverse datasets. The superior performance confirms that **hierarchical fixed-window scanning** stabilizes the process and captures IQA-relevant structures more effectively.\n\nWe will add this clarification and an architectural illustration in the revised version to highlight the hierarchical nature of our design.\n\n\n**Q2: Clarifying the Performance Difference Between QMamba and LQMamba.**\n\n**A2:** We sincerely appreciate the reviewer’s valuable observation. While the average performance gap between QMamba and LQMamba appears marginal in Table 1 and Table 2, a closer examination reveals more nuanced insights.\n\nIn fact, LQMamba outperforms QMamba on most individual datasets. The seemingly negligible overall improvement stems primarily from relatively inferior performance on small and simple datasets such as LIVE and CSIQ, which lowers the averaged metrics. These datasets contain fewer distortion types (e.g., 5 in LIVE vs. 25 in KADID-10k) and tend to feature less challenging scenarios where local distortion-sensitive modeling (as introduced by LQMamba) cannot fully demonstrate its advantage.\n\nHowever, in more complex datasets like TID2013 and KADID-10k — which include a broader range of fine-grained distortions — LQMamba consistently shows stronger perceptual performance. For example:\n\n- TID2013: QMamba-B (0.949), LQMamba-B (0.964)\n- KADID: QMamba-B (0.932), LQMamba-B (0.941)\n\nThis suggests that LQMamba’s local scanning scheme is especially beneficial in challenging real-world conditions, where local artifacts are more critical and nuanced. Hence, we believe LQMamba is an optional and complementary alternative to QMamba, particularly suitable for scenarios requiring finer local distortion modeling.\n\nWe will clarify this phenomenon with detailed dataset-level breakdowns and further analysis in the revised version to avoid potential misunderstandings and better highlight the advantage of the proposed local scan mechanism.\n\n**Q3: About the Comparison with Suggested Methods**\n\n**A3:** We sincerely appreciate the reviewer’s suggestion of several creative and inspiring methods. To provide a fair comparison, we refer to the PLCC results reported in their original papers. As shown in the table below, our proposed QMamba consistently achieves leading performance on most datasets, demonstrating its strong generalization ability across various distortion types and data distributions.\n\n| Method    | TID2013 | KADID | CLIVE | KonIQ | SPAQ  |\n|-----------|---------|-------|-------|-------|-------|\n| Re-IQA    | 0.880   | 0.892 | 0.854 | -     | 0.925 |\n| QPT       | -       | -     | **0.914** | 0.941 | 0.927 |\n| LIQE      | -       | 0.931 | 0.910 | 0.908 | -     |\n| **Ours**  | **0.965** | **0.943** | 0.913 | **0.947** | **0.934** |\n\n\n*Table 2. PLCC comparison of different methods across multiple datasets.*\n\nWe believe these results demonstrate the strong performance and versatility of our approach across various datasets.", "claims_and_evidence": "The style prompt injection is simple yet effective idea and the effectiveness of it is demonstrated in table 3.\n\nI'm uncertain the LQMamba is a brand new arcitecture because it is similar to LocalMamba.\nWhat is the key difference between LQMamba and LocalMamba?\n\nAre there any advantages or characteristics of LQMamba compared to QMamba? These aspects are not shown or discussed in the paper.", "methods_and_evaluation_criteria": "The proposed method is evaluated on ten popular IQA datasets and this is enough amount.", "theoretical_claims": "The style prompt tuning paradigm, which is a kind of intrinsic style manipulation, seems reasonable because similar types of manipulation have been utilized since the past, like in StyleGAN.", "experimental_designs_or_analyses": "The achievement of SOTA performance and the t-SNE results are reasonably good.\nIn addition, the proposed method shows significant improvement in cross-validation test according to table3. \n\nAccording to table1 and table2, the difference between QMamba and LQMamba seems neglectable even though their scanning approach is very different, and this phenomenon is not discussed in detail.", "relation_to_broader_scientific_literature": "By demonstrating that vision mamba-based models contribute to improved IQA performance, particularly in cross-validation performance, this may facilitate the advancement of new IQA model architectures.", "essential_references_not_discussed": "Missing IQA methods.\n- Re-iqa: Unsupervised learning for image quality assessment in the wild\n- Quality-aware pre-trained models for blind image quality assessment\n- Blind image quality assessment via vision-language correspondence: A multitask learning perspective", "other_strengths_and_weaknesses": ".", "other_comments_or_suggestions": ".", "questions_for_authors": ".", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "5NBuHNYql8", "reviewer": "Reviewer_BfKX", "summary": "According to this work, it is the first to introduce the Mamba architecture into IQA and proposes the StylePrompt Tuning Mechanism to enhance transfer capability. The proposed method achieves better results with lower FLOPS across multiple datasets.", "questions": "", "limitations": "", "rating": 2, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "**Q1: Some clarification about the suboptimal performance on some small datasets, i.e., LIVE, CSIQ, LIVEFB.**\n\n**A1:** Thanks for your great suggestions. We will clarify the reasons for the suboptimal performance of our method on some classical datasets, e.g., LIVE, CSIQ, and LIVEFB with similar parameters/flops.\n\n**(i)** First, we have to argue that it is very challenging for all existing IQA models to achieve the best performances on all IQA datasets due to the different dataset distributions and sizes, which focus on different capabilities of the IQA model, e.g., local/global representation modeling, distortion perception, etc. However, our Mamba-based model can achieve optimal performances on 5 among 8 datasets, especially for the challenging three real-world datasets and two large-scale IQA datasets, showing obvious advantages compared with other methods, which only achieve optimal performances on 1 or 2 classic datasets.\n\n**(ii)** Secondly, our proposed LQMamba backbone is actually a fundamental framework, which is complementary to other strategies instead of the backbone design. It is noteworthy that existing works achieving great performances on traditional small datasets, e.g., LIVE, and CSIQ, have introduced some strategies over the backbones to improve the data-efficient training. For example, LoDa introduces the hybrid architecture of CNN and Transformer to extract the hierarchical representation of degradation, thereby achieving great performance on LIVE. DEIQT introduces multiple attention panels to extract different quality perspectives, which enhances the quality assessment of transformer-based architecture. However, these strategies can be theoretically applied to our Mamba-based architecture, which further enhances the capability of our method on some traditional datasets.\n\n**(iii)** Actually, the real-world IQA datasets and large-scale IQA datasets are more aligned with practical applications, which can demonstrate the potential and generalization of designed models in the real world. From the experiments in Table 1, our Mamba-based method can achieve optimal/second optimal performances on all real-world IQA datasets and large-scale IQA datasets. These reveal the effectiveness of our Q-Mamba compared with existing works.\n\n**(iv)** From the technical contribution, instead of the first Mamba-based IQA backbone, we also propose StylePrompt, a lightweight tuning paradigm that enables effective cross-domain transfer using only 4% of the total parameters, while achieving near full fine-tuning performance.\n\nWe will further improve the generalization capability of Q-Mamba and excavate its potentials on some classic IQA datasets in the future.\n\n**Q2: Suggestion about Comparisons with Recent Methods.**\n\n**A2:** We thank the reviewer for the helpful suggestions. We have carefully reviewed and compared our method with the suggested recent works, including TOPIQ, RichIQA, AHIQ, and SF-IQA.\n\n| **Method**  | **LIVE** | **CSIQ** | **TID2013** | **CLIVE** | **KonIQ-10k** | **SPAQ** |\n|-------------|----------|----------|-------------|-----------|---------------|----------|\n| TOPIQ       | 0.984    | 0.980    | 0.958       | 0.884     | 0.939         | 0.924    |\n| RichIQA     | -        | -        | -           | 0.912     | 0.950         | 0.923    |\n| Ours        | 0.962    | 0.940    | 0.965       | 0.913     | 0.947         | 0.934    |\n\nAlthough TOPIQ achieves better results than QMamba on two small-scale datasets (LIVE and CSIQ), we outperform it on more complex and diverse datasets such as **TID2013**, **CLIVE**, **KonIQ-10k**, and **SPAQ**, which are more representative of real-world IQA challenges. We believe this reflects the stronger generalization and robustness of our model.\n\nAdditionally, AHIQ and SF-IQA are tailored for specific competition settings and only report results on a few small datasets (1–3), often under special constraints. In contrast, we evaluate our method on **10 datasets**, covering a broad range of **synthetic, authentic, and AIGC-related distortions**.\n\nHence, while we appreciate the value of these recent works, we believe that our comprehensive, consistent, and large-scale evaluation across diverse scenarios offers a more complete and robust comparison. Our results suggest that QMamba is highly competitive and practically effective across both standard and challenging IQA tasks.", "claims_and_evidence": "The proposed method does not achieve leading performance on classic datasets such as LIVE, CSIQ, and LIVEFB under similar parameter counts or FLOPS.", "methods_and_evaluation_criteria": "It follows established protocols.", "theoretical_claims": "I checked the method part and the corrsponding formulas. There are no significant issues.", "experimental_designs_or_analyses": "Experimental designs make sense, including the quantitative results in Table 1~4, and the visualizations in Figure 3.", "relation_to_broader_scientific_literature": "It explores the application of the Mamba design in the IQA field, which may provide some inspiration for future research on IQA architectures.", "essential_references_not_discussed": "Some recent comparisons are missed. For instance,\n\n[1] TOPIQ: A Top-Down Approach From Semantics to Distortions for Image Quality Assessment\n\n[2] Attention Helps CNN See Better: Hybrid Image Quality Assessment Network\n\n[3] Exploring Rich Subjective Quality Information for Image Quality Assessment in the Wild\n\n[4] SF-IQA: Quality and Similarity Integration for AI Generated Image Quality\nAssessment\n\nSome of these methods achieve better results on some datasets and metrics.", "other_strengths_and_weaknesses": "This work explores the application of the Mamba structure in IQA tasks. However, overall, its effectiveness on some datasets has not been fully demonstrated, and I believe this work still requires further refinement.", "other_comments_or_suggestions": "Please refer to the strengths and weaknesses.", "questions_for_authors": "Please refer to the strengths and weaknesses.", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Meta-review: This paper proposes QMamba, the first exploration of the Mamba state space model for no-reference image quality assessment (NR-IQA). With a lightweight StylePrompt tuning strategy, QMamba achieves strong performance and transferability across various IQA tasks, outperforming prior methods. Three out of four reviews are positive. The remaining reviewer maintains a negative score, citing that while the authors provided comprehensive comparisons on 10 datasets, the rebuttal results only covered 6 datasets. The reviewer argues that methods like TopIQ and RichIQA showed competitive or better results in some cases, and without full comparisons, it's hard to assess QMamba's overall advantage. Despite this, the AC believes the strengths of the paper outweigh the concerns and recommends acceptance. The authors are expected to address these issues more thoroughly and include the rebuttal content in the camera-ready version.
Decision: Accept (poster)