Journal of Computer Applications ›› 2016, Vol. 36 ›› Issue (3): 789-794.DOI: 10.11772/j.issn.1001-9081.2016.03.789

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No-reference image quality assessment based on scale invariance

TIAN Jinsha1, HAN Yongguo1, WU Yadong1, ZHAO Xiaole1, ZHANG Hongying2   

  1. 1. School of Computer Science and Technology, Southwest University of Science and Technology, Mianyang Sichuan 621010, China;
    2. School of Information Engineering, Southwest University of Science and Technology, Mianyang Sichuan 621010, China
  • Received:2015-08-25 Revised:2015-09-21 Online:2016-03-10 Published:2016-03-17
  • Supported by:
    This work is partially supported by the National Natural Science Foundation of China (61303127), Sichuan Science and Technology Support Program (2014GZ0100,2014SZ0223), the "West Light" Training Project of Chinese Academy of Sciences (13ZS0106), the Major Project of Education Office Sichuan Province (13ZA0169).


田金沙1, 韩永国1, 吴亚东1, 赵小乐1, 张红英2   

  1. 1. 西南科技大学 计算机科学与技术学院, 四川 绵阳 621010;
    2. 西南科技大学 信息工程学院, 四川 绵阳 201010
  • 通讯作者: 吴亚东
  • 作者简介:田金沙(1988-),女,河北衡水人,硕士研究生,主要研究方向:数字图像处理;韩永国(1963-),男,四川遂宁人,教授,博士,主要研究方向:虚拟现实、仿真;吴亚东(1979-),男,河南周口人,教授,博士,CCF会员,主要研究方向:图像图形处理、信息可视化、人机交互;赵小乐(1987-),男,四川南部人,硕士研究生,CCF会员,主要研究方向:数字图像处理;张红英(1976-),女,四川德阳人,教授,博士,主要研究方向:图像处理。
  • 基金资助:

Abstract: The existing general no-reference image quality assessment methods mostly use machine learning method to learn regression models from training images with associated human subjective scores to predict the perceptual quality of testing image. However, such opinion-aware methods expend much time on training, and rely on the distortion types of the training database. These methods have weak generalization capability, hereby limiting their usability in practice. To solve the database dependence, a normalized scale invariance based no-reference image quality assessment method was proposed. In the proposed method, the Natural Scene Statistic (NSS) feature and edge characteristic were combined as the valid features for image quality assessment, and no extra information was required beyond the testing image, then the two feature vectors were used to compute the global difference across scales as the image quality score. The experimental results show that the proposed method has good evaluation for multi-distorted images with low computational complexity. Compared to the state-of-the-art no-reference image quality assessment models, the proposed method has better comprehensive performance, and it is suitable for applications.

Key words: multi-scale, no-reference, image quality assessment, Natural Scene Statistic (NSS), structure feature

摘要: 现有的通用型无参考图像质量评价方法大多是利用失真图像及其主观值来训练回归模型预测图像质量指标,然而这种方法需要消耗大量的时间进行训练,并且评价效果依赖于训练图像库中的失真类型,通用性较差,很难应用到实际场合中。为了解决数据库依赖问题,提出一种归一化的基于图像尺度不变性的无参考图像质量评价方法。该方法不依赖外部数据,将图像的统计特性及边缘结构特性作为图像质量评价的有效特征,利用图像多尺度不变性计算多尺度间的整体特征差异,从而预测图像质量。实验结果表明,所提方法对混合失真图像质量评价效果好,运行效率高,与目前现有的无参考图像质量评估方法相比具有较好的综合性能,具有较好的应用价值。

关键词: 多尺度, 无参考, 图像质量评估, 自然场景统计特性, 结构特征

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