计算机工程2026，Vol.52Issue(3)：107-118,12.DOI:10.19678/j.issn.1000-3428.0069763
基于多尺度金字塔池化的自适应无参考图像质量评价
Adaptive No-Reference Image Quality Assessment Based on Multi-Scale Pyramid Pooling
摘要
Abstract
In the Image Quality Assessment(IQA),no-reference quality assessment methods have demonstrated significant application value and development potential for managing distorted images in real-world scenarios.However,real-world distorted images exhibit high diversity and complexity,which make designing relevant evaluation algorithms more difficult.In recent years,deep learning technology has achieved remarkable success in various subfields of image processing,such as image classification,object detection,and image segmentation.These advancements have motivated researchers to introduce Deep Neural Network(DNN)technology into IQA.Owing to their outstanding feature extraction and learning capabilities,DNNs have provided innovative solutions and made significant progress in the quality assessment of distorted images in real-world environments.Despite these advancements,existing methods still have certain limitations in describing the image quality in real-world scenes,particularly when handling diverse image content.Additionally,many DNN-based IQA methods require the input images to be scaled or cropped to a fixed resolution,which often compromises the original structure and content of the images,thereby affecting the accuracy and generalizability of the quality assessment.To address these issues,this paper proposes an adaptive No-Reference IQA(NR-IQA)method based on Multi-Scale Pyramid Pooling(MSPP-IQA).This method does not require preprocessing and can assess the quality of an image in its original size.Furthermore,by introducing content understanding and attention modules,MSPP-IQA can mimic the working principles of the Human Visual System(HVS),simultaneously perceiving global high-level and local low-level features.Experimental results demonstrate that,compared to current mainstream methods,MSPP-IQA performs well on both real-world and synthetic distortion datasets.These results validate the effectiveness and superiority of MSPP-IQA in addressing the challenges in assessing the quality of real-world distorted images.关键词
无参考图像质量评价/真实失真/多尺度特征融合/空间金字塔池化/注意力机制Key words
No-Reference Image Quality Assessment(NR-IQA)/real distortion/multi-scale feature fusion/Spatial Pyramid Pooling(SPP)/attention mechanism分类
信息技术与安全科学引用本文复制引用
吴雪松,陈媛媛,周涛..基于多尺度金字塔池化的自适应无参考图像质量评价[J].计算机工程,2026,52(3):107-118,12.基金项目
国家自然科学基金(62376173). （62376173）
