光学精密工程2025，Vol.33Issue(1)：107-122,16.DOI:10.37188/OPE.20253301.0107
基于纹理奇异值分解的全参考图像质量评价
Full-reference image quality assessment based on texture singular value decomposition
摘要
Abstract
In industrial vision systems,subjective assessment is costly,pre-training for no-reference quali-ty evaluation is time-intensive,and there is a critical need for a highly accurate full-reference image quality assessment model.To address these challenges,this study proposes a novel full-reference image quality assessment model based on singular value decomposition(SVD)with weighted texture information.First,SVD is applied to the reference image blocks,and the singular values of the distorted blocks are esti-mated using the singular vectors of both the reference and distorted image blocks,yielding the brightness similarity component.Next,the estimated singular values of the distorted image blocks are used to quanti-fy average offset distortion and contrast change distortion,resulting in the contrast similarity component.The structural similarity of the images is then determined by analyzing the deviation of the singular vectors of the distorted image blocks from the unit matrix of the reference image blocks.Finally,the brightness,contrast,and structural similarity components are weighted using texture information to construct the full-reference image quality assessment model.The proposed method was evaluated on six widely used image quality assessment databases across four performance criteria.Experimental results demonstrate that the model achieves a weighted Spearman rank correlation coefficient of 0.896 3 across the datasets.For con-trast change distortion,the model attains a Spearman rank correlation coefficient of 0.859 5,outperform-ing the second-best method by 85%.Compared to existing full-reference image quality assessment mod-els,the proposed approach offers significant advantages in prediction accuracy,generalization capability,and computational efficiency.关键词
图像质量评价/全参考/奇异值分解/纹理信息/图像对比度Key words
image quality assessment/full reference/singular value decomposition/texture information/image contrast分类
计算机与自动化引用本文复制引用
李佳欣,段发阶,傅骁,牛广越..基于纹理奇异值分解的全参考图像质量评价[J].光学精密工程,2025,33(1):107-122,16.基金项目
国家自然科学基金资助项目(No.52205573,No.U2241265,No.92360306,No.61971307,No.62231011) （No.52205573,No.U2241265,No.92360306,No.61971307,No.62231011）
中国博士后科学基金资助项目(No.2022M720106) （No.2022M720106）
天津大学科技创新领军人才培育"启明计划"项目(No.2024XQM-0012) （No.2024XQM-0012）
精密测试技术及仪器全国重点实验室(天津大学)青年教师科研启动项目(No.Pilq2304) （天津大学）
国家科技重大专项(No.J2022-V-0005-0031) （No.J2022-V-0005-0031）
航空科学基金资助项目(No.2022Z060048001) （No.2022Z060048001）
青年人才托举工程资助项目(No.2021QNRC001) （No.2021QNRC001）
装备预研教育部联合基金资助(No.8091B022144) （No.8091B022144）
国防科技重点实验室基金资助项目(No.6142212210304) （No.6142212210304）
广东省重点研发计划项目(No.2020B0404030001) （No.2020B0404030001）
霍英东教育基金会资助项目(No.171055) （No.171055）
