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

JIA Huizhen; ZHAO Yuxuan; FU Peng; WANG Tonghan

doi:10.11999/JEIT251220

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JIA Huizhen, ZHAO Yuxuan, FU Peng, WANG Tonghan. PSAQNet: A Perceptual Structure Adaptive Quality Network for Authentic Distortion Oriented No-reference Image Quality Assessment[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT251220

Citation:

JIA Huizhen, ZHAO Yuxuan, FU Peng, WANG Tonghan. PSAQNet: A Perceptual Structure Adaptive Quality Network for Authentic Distortion Oriented No-reference Image Quality Assessment[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT251220

Citation:

JIA Huizhen, ZHAO Yuxuan, FU Peng, WANG Tonghan. PSAQNet: A Perceptual Structure Adaptive Quality Network for Authentic Distortion Oriented No-reference Image Quality Assessment[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT251220

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

doi: 10.11999/JEIT251220 cstr: 32379.14.JEIT251220

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)

Received Date: 2025-11-19
Accepted Date: 2026-01-30
Rev Recd Date: 2026-01-30

Available Online: 2026-02-12

Abstract

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

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References(30)

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