PSAQNet：面向真实失真无参考图像质量评价的感知结构自适应质量网络

贾惠珍; 赵宇轩; 傅鹏; 王同罕

doi:10.11999/JEIT251220

PSAQNet：面向真实失真无参考图像质量评价的感知结构自适应质量网络

doi: 10.11999/JEIT251220 cstr: 32379.14.JEIT251220

1.
东华理工大学人工智能与信息工程学院南昌 330013
2.
南京理工大学计算机科学与工程学院南京 210094

基金项目: 国家自然科学基金(62266001, 62261001)

详细信息

作者简介:
贾惠珍：女，副教授，研究方向为计算机视觉、模式识别、图像质量评价

赵宇轩：男，硕士生，研究方向为图像质量评价、计算机视觉

傅鹏：男，副教授，研究方向为模式识别与图像处理

王同罕：男，副教授，研究方向为计算机视觉、模式识别、图像质量评价

通讯作者:
王同罕　thwang@ecut.edu.cn

中图分类号: TP391; TN911.7
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- 被引次数: 0
出版历程
- 收稿日期: 2025-11-19
- 修回日期: 2026-01-30
- 录用日期: 2026-01-30
- 网络出版日期: 2026-02-12

PSAQNet: A Perceptual Structure Adaptive Quality Network for Authentic Distortion Oriented No-reference Image Quality Assessment

1.
School of Artificial Intelligence and Information Engineering, East China University of Technology, Nanchang 330013, China
2.
School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China

Funds: The National Natural Science Foundation of China (62266001, 62261001)

摘要

摘要: 针对无参考图像质量评价方法在真实场景中存在鲁棒性不够、泛化能力不足、几何结构建模欠缺的问题，该文提出一种基于感知结构自适应质量网络(PSAQNet)的无参考图像质量评价方法。首先，利用预训练卷积神经网络(CNN)提取多尺度特征，并通过高级失真增强模块对多分支特征进行门控筛选与适配，突出与失真相关的区域、抑制无关干扰；其次，引入通道感知自适应核卷积与空间引导卷积，从通道重标定、自适应采样以及空间引导调制等角度增强对旋转、扭曲等几何退化的建模与对齐能力；接着，将增强后的多尺度卷积特征经自适应池化与投影转换为token序列，并通过交叉注意力机制与Transformer全局表示进行选择性交互，实现局部细节与全局语义的有效融合；最后，在融合过程中结合分组卷积注意力进一步强调失真显著区域，通过预测头回归得到图像质量分数。在6个经典的数据库上进行实验结果显示，PSAQNet在皮尔逊线性相关系数(PLCC)/斯皮尔曼秩相关系数(SRCC)等相关性指标上优于多种代表性无参考图像质量评价方法。尤其在复杂失真和跨数据库测试中展现出更强的鲁棒性与泛化能力。
- 图像质量评价 /
- 失真感知 /
- 几何建模 /
- 通道感知
Abstract: Objective No-Reference Image Quality Assessment (NR-IQA) is critical for practical imaging systems when pristine reference images are unavailable. However, many existing methods face three major challenges: limited robustness under complex distortions, weak generalization when distortion distributions shift (e.g., from synthetic to real-world settings), and insufficient modeling of geometric or structural degradations such as spatially varying blur, misalignment, and texture-structure coupling. These limitations cause models to rely excessively on dataset-specific statistics and reduce their effectiveness when applied to diverse scenes with mixed degradations. To address these issues, the Perceptual Structure Adaptive Quality Network (PSAQNet) is proposed to improve the accuracy and adaptability of NR-IQA under complex distortion conditions. Methods PSAQNet is designed as a unified CNN-Transformer framework that preserves hierarchical perceptual cues and supports global context reasoning. Instead of relying on late-stage pooling, distortion evidence is progressively enhanced throughout the network. The architecture contains several key components. The Advanced Distortion Enhanced Module (ADEM) operates on multi-scale features extracted from a pre-trained backbone. It adopts multi-branch gating and a distortion-aware adapter to emphasize degradation-related signals and reduce interference from dominant image content. This mechanism dynamically selects feature branches that correspond to perceptual degradation patterns, which is beneficial for spatially non-uniform or mixed distortions. To model geometric degradations, PSAQNet integrates Spatial-Guided Convolution (SGC) and Channel-Aware Adaptive Kernel convolution (CA_AK). SGC improves spatial sensitivity by guiding convolutional responses with structure-aware cues and focusing on regions where geometric distortions are prominent. CA_AK further improves geometric modeling by adaptively adjusting receptive behavior and recalibrating channels to preserve distortion-sensitive components. Additionally, PSAQNet incorporates efficient feature fusion strategies. Group Convolutional Block Attention Module (GroupCBAM) enables lightweight attention-based fusion of multi-level CNN features, whereas AttInjector selectively injects local distortion cues into global Transformer representations. This design allows global semantic reasoning to be guided by localized degradation evidence without introducing redundancy or instability. Results and Discussions Extensive experiments on six benchmark datasets containing both synthetic and real-world distortions demonstrate that PSAQNet achieves strong performance and stable agreement with human subjective judgments. The proposed method outperforms several recent approaches, particularly on real-world distortion datasets. These results indicate that PSAQNet effectively enhances distortion evidence, models geometric degradation, and integrates local distortion cues with global semantic representations. Such capabilities improve robustness under distribution shifts and reduce reliance on narrow distortion priors. Ablation studies confirm the contribution of each module. ADEM increases distortion saliency, SGC and CA_AK improve sensitivity to geometric degradations, and GroupCBAM and AttInjector strengthen the interaction between local and global features. Cross-dataset evaluations further demonstrate the generalization capability of PSAQNet across different content categories and distortion types. Scalability experiments also show that the framework benefits from stronger pretrained backbones without compromising its modular design. Conclusions PSAQNet addresses several key limitations in NR-IQA by integrating local distortion enhancement, geometric-aware feature modeling, and global semantic fusion within a unified framework. The modular architecture improves robustness and generalization across diverse distortion conditions and supports practical deployment in real-world scenarios. Future work will explore vision–language pre-training to improve cross-scene adaptability.
- Image quality assessment /
- Distortion perception /
- Geometric modeling /
- Channel perception

HTML全文

图 1 PSAQNet模型结构

下载: 全尺寸图片幻灯片

图 2 高级失真增强模块

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图 3 空间-通道自适应提取器

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图 4 空间引导卷积模块

下载: 全尺寸图片幻灯片

图 5 通道感知自适应核卷积

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图 6 注意力引导注入模块

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图 7 分组卷积块注意力模块

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表 1 本实验使用的6个数据集

数据集	失真图像数	失真类型数	数据集类型
LIVE	779	5	合成失真
CSIQ	866	6	合成失真
TID2013	3000	24	合成失真
KADID-10k	10125	25	合成失真
LIVEC	1162	-	真实失真
KonIQ-10k	10073	-	真实失真

下载: 导出CSV

表 2 与经典和最新的模型的对比结果

方法	CSIQ		TID2013		LIVE		KADID-10k		LIVEC		KonIQ-10k
方法	PLCC	SRCC	PLCC	SRCC	PLCC	SRCC	PLCC	SRCC	PLCC	SRCC	PLCC	SRCC
TReS	0.942	0.922	0.883	0.863	0.968	0.969	0.858	0.859	0.877	0.846	0.928	0.915
LoDa	0.968	0.961	0.901	0.869	0.979	0.975	0.936	0.931	0.887	0.871	0.934	0.920
SaTQA	0.972	0.965	0.931	0.948	0.983	0.983	0.949	0.946	0.899	0.877	0.941	0.930
MDM-GFIQA	0.973	0.965	0.936	0.929	0.976	0.976	0.921	0.918	0.908	0.887	0.942	0.930
本文	0.979	0.974	0.938	0.929	0.988	0.987	0.942	0.937	0.908	0.887	0.943	0.935

下载: 导出CSV

表 3 消融实验结果

方法	KonIQ-10k		TID2013
方法	PLCC	SRCC	PLCC	SRCC
baseline	0.934	0.920	0.901	0.869
baseline+DEM+CA_AK	0.936	0.925	0.924	0.922
baseline+DEM+SGC	0.934	0.927	0.925	0.920
baseline+CA_AK+SGC	0.936	0.924	0.922	0.919
baseline+DEM+CA_AK+SGC	0.938	0.927	0.932	0.925
baseline+DEM+CA_AK+ GroupCBAM	0.936	0.930	0.933	0.923
baseline+DEM+SGC+ GroupCBAM	0.939	0.931	0.930	0.926
PSAQNet	0.943	0.935	0.938	0.929

下载: 导出CSV

表 4 泛化性能测评

训练集	KonIQ-10K	KADID-10k	LIVE
测试集	LIVEC	KonIQ-10k	CSIQ	TID2013
HyperIQA	0.785	0.648	0.744	0.551
TReS	0.786	0.606	0.761	0.562
LoDa	0.794	0.654	0.823	0.621
SaTQA	0.791	0.661	0.831	0.627
MDM-GFIQA	0.813	0.670	0.840	0.641
本文	0.817	0.677	0.842	0.659

下载: 导出CSV

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