通信学报2026，Vol.47Issue(8)：47-59,13.DOI:10.11959/j.issn.1000-436x.TXXB260211
差异化建模与感知增强的轻微失真盲图像质量评价
Differential modeling and perception enhancement for slight-distortion blind image quality assessment
摘要
Abstract
Despite significant progress in mainstream blind image quality assessment(BIQA)algorithms,performance bottlenecks persist when evaluating slightly distorted images.This stems from feature modeling conflicts induced by global training strategies:the human visual system(HVS)focuses on global degradation for severe distortions but relies heavily on local discrepancies and semantic content for slight ones.To address this,a BIQA framework was proposed based on differential modeling(DM)and perception enhancement.Firstly,a DM strategy constructed a dual-branch ar-chitecture to decouple slight and severe distortion representations,mitigating cross-level conflicts.Secondly,a semantic guidance perception enhancement(SGPE)module adaptively directed the model to focus on semantically sensitive re-gions,enhancing sensitivity to subtle quality variations.Finally,a distortion-adaptive weighted fusion(DAWF)strategy dynamically aggregated features,significantly improving slight-distortion accuracy while maintaining robust prediction across the full distortion spectrum.Experiments on standard benchmark datasets demonstrate that the proposed method effectively overcomes the slight-distortion bottleneck and exhibits strong generalization capability.关键词
盲图像质量评价/差异化建模/轻微失真/感知增强Key words
blind image quality assessment/differential modeling/slight-distortion/perception enhancement分类
信息技术与安全科学引用本文复制引用
程德强,陈飞宇,宋天舒,寇旗旗..差异化建模与感知增强的轻微失真盲图像质量评价[J].通信学报,2026,47(8):47-59,13.基金项目
国家自然科学基金资助项目(No.62501597,No.52204177) （No.62501597,No.52204177）
江苏省基础研究计划(No.BK20251651) （No.BK20251651）
江苏省卓越博士后计划(No.2024ZB423) （No.2024ZB423）
中国博士后科学基金资助项目(No.2024M763543) The National Natural Science Foundation of China(No.62501597,No.52204177),The Basic Research Program of Jiangsu Province(No.BK20251651),The Jiangsu Funding Program for Excellent Postdoctoral Talent(No.2024ZB423),The China Postdoctoral Science Foundation(No.2024M763543) （No.2024M763543）
