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全参考图像质量评价回顾与展望
Review and Prospect of Full Reference Image Quality Assessment

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高敏娟 1   党宏社 1   魏立力 2   刘国军 2   张选德 3 *  
文摘 全参考图像质量评价(Full Reference Image Quality Assessment, FR-IQA)是IQA领域广为研究的类型之一.本文回顾了FR-IQA的发展历程,对FR-IQA应用现状和通用FR-IQA问题的构建进行综述,以及对FR-IQA算法进行总结和梳理.并在此基础上,重点分析了现有研究中存在的问题,包括问题构建的合理性、建模的全面性问题、知识驱动与数据驱动结合的问题等.基于对主观评价过程的深入分析,结合现有研究存在的问题,探讨了主观评分采用模糊建模和知识与数据联合驱动构建算法两个可能的研究方向,以期对后续的研究者提供参考.
其他语种文摘 Full reference image quality assessment (FR-IQA) is one of the types widely studied in the field of IQA. This paper reviews the development of FR-IQA, summarizes the application status of FR-IQA and the construction of general FR-IQA problems, and summarizes and combs FR-IQA algorithms. And on this basis, it focuses on the problems existing in the existing research, including the rationality of the problem construction, the comprehensive problem of modeling, and the problem of the combination of knowledge-driven and data-driven. Finally, based on the in-depth analysis of the subjective evaluation process and the existing problems in the existing research, the two possible research directions of subjective scoring using fuzzy modeling and knowledge-data-driven construction algorithm are discussed, in order to provide reference for subsequent researchers.
来源 电子学报 ,2021,49(11):2261-2272 【核心库】
DOI 10.12263/DZXB.20200780
关键词 图像质量评价 ; FR-IQA ; 主观评分 ; 模糊建模 ; 知识与数据联合驱动
地址

1. 陕西科技大学电气与控制工程学院, 陕西, 西安, 710021  

2. 宁夏大学数学统计学院, 宁夏, 银川, 750021  

3. 陕西科技大学电子信息与人工智能学院, 陕西, 西安, 710021

语种 中文
文献类型 综述型
ISSN 0372-2112
学科 自动化技术、计算机技术
基金 国家自然科学基金 ;  陕西省自然科学基金
文献收藏号 CSCD:7109411

参考文献 共 78 共4页

1.  Chandler D M. Seven challenges in image quality assessment: Past, present, and future research. ISRN Signal Processing,2013,2013:1-53 CSCD被引 15    
2.  Mohammadi P. Subjective and objective quality assessment of image: A survey. Majlesi Journal of Electrical Engineering,2014,9(1):419-423 CSCD被引 1    
3.  Mannos J. The effects of a visual fidelity criterion of the encoding of images. IEEE Transactions on Information Theory,1974,20(4):525-536 CSCD被引 76    
4.  Buades A. A review of image denoising algorithms, with a new one. Multiscale Modeling & Simulation,2005,4(2):490-530 CSCD被引 299    
5.  李亮亮. 基于非下采样剪切波变换的图像增强算法研究,2019 CSCD被引 6    
6.  Wang Z. Image quality assessment: From error visibility to structural similarity. IEEE Transactions on Image Processing,2004,13(4):600-612 CSCD被引 2525    
7.  沈瑜. 基于混合模型驱动的红外与可见光图像融合. 控制与决策,2021,36(9):2143-2151 CSCD被引 5    
8.  Ding K Y. Comparison of fullreference image quality models for optimization of image processing systems. International Journal of Computer Vision,2021,129(4):1258-1281 CSCD被引 7    
9.  Zhao H. Loss functions for image restoration with neural networks. IEEE Transactions on Computational Imaging,2017,3(1):47-57 CSCD被引 132    
10.  Wang Z. Multiscale structural similarity for image quality assessment. The Thrity-Seventh Asilomar Conference on Signals, Systems & Computers,2003:1398-1402 CSCD被引 14    
11.  Laparra V. Perceptual image quality assessment using a normalized Laplacian pyramid. Electronic Imaging,2016,2016(16):1-6 CSCD被引 1    
12.  Laparra V. Perceptually optimized image rendering. Journal of the Optical Society of America A: Optics, Image Science, and Vision,2017,34(9):1511-1525 CSCD被引 4    
13.  Ma J J. Diagnostic image quality assessment and classification in medical imaging: Opportunities and challenges. 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI),2020:337-340 CSCD被引 1    
14.  郭从洲. 遥感图像质量等级分类的深度卷积神经网络方法. 武汉大学学报(信息科学版),2020 CSCD被引 1    
15.  李毅红. 基于递变能量线性约束的X射线图像质量评价方法. 电子学报,2017,45(3):669-673 CSCD被引 2    
16.  Sheikh H R. Image and video quality assessment research at LIVE,2020 CSCD被引 1    
17.  Larson E C. Categorical subjective image quality database CSCD被引 1    
18.  Ponomarenko N. Tampere image database TID2008,2020 CSCD被引 1    
19.  Ponomarenko N. Image database TID2013: Peculiarities, results and perspectives. Signal Processing: Image Communication,2015,30:57-77 CSCD被引 53    
20.  Ninassi A. Subjective quality assessment IVC database 2005,2020 CSCD被引 1    
引证文献 6

1 吴靖 基于深度学习的单帧图像超分辨率重建综述 电子学报,2022,50(9):2265-2294
CSCD被引 7

2 江本赤 基于色貌尺度相位一致性的全参考图像质量评价 光学精密工程,2023,31(10):1509-1521
CSCD被引 2

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