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浙江大学学报(工学版)
J4  2009, Vol. 43 Issue (5): 864-868    DOI: 10.3785/j.issn.1008-973X.2009.05.015
自动化技术、计算机技术     
基于失真模型的结构相似度图像质量评价
楼斌1,2,沈海斌1,赵武锋1,严晓浪1
（1.浙江大学 超大规模集成电路设计研究所,浙江 杭州 310027; 2.杭州电子科技大学 计算机学院，浙江 杭州 310018）
Structural similarity image quality assessment based on distortion model
LOU Bin1,2, SHEN Hai-bin1, ZHAO Wu- feng1, YAN Xiao-lang1
(1.Institute of VLSI Design, Zhejiang University, Hangzhou 310027, China;
2. School of Computer and Software, Hangzhou Dianzi University, Hangzhou 310018, China)
 全文: PDF(1180 KB)  
摘要：

提出了一种基于图像失真模型及失真视觉特性的图像质量评价方法,解决了传统结构相似度（SSIM）度量不能同时有效评价不同失真强度与不同失真类型图像质量的问题.将图像失真分解为局部线性模糊及加性噪声,通过质量敏感区域加权与噪声SSIM补偿,实现各种失真类型SSIM的聚合以提高综合评价性能.实验结果表明,这种基于失真模型的区域加权SSIM能够一致评价各种失真类型、各种失真强度的图像质量,在LIVE图库上与主观评价分回归后的相关系数达到0.946 7,优于其他SSIM算法.
关键词： 结构相似度（SSIM）;  图像质量评价;  图像失真模型;  人类视觉系统（HVS）    
Abstract:

This work proposed a new structural similarity (SSIM) method, which is adapted for assessing images of different distortion types and different distortion intensities.  The new method models a distorted image as an original image that subjects to linear frequency distortion (LFD) and additive noise injection (NI). LFD is local, and the SSIM of  this type can be clustered by being weighted on quality sensitive regions. NI is uncorrelated with the original image, and the image quality of this type is underestimated by  SSIM. So quality compensation is used to unify SSIM metric of these two types. Finally, the new method was validated with subjective quality scores on LIVE database which  containing 982 images. Experimental results showed that the performance of the new method is comparable with the art-of-the-state objective methods．
Key words: structural similarity (SSIM)    image quality assessment    image distortion model    human vision system (HVS)
出版日期: 2009-06-01
:  TP391  
通讯作者: 沈海斌，男，副教授.     E-mail: shb@vlsi.zju.edu.cn
作者简介: 楼斌（1982－），男，浙江义乌人，博士，从事图像处理技术研究.
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引用本文:

楼斌, 沈海斌, 赵武锋, 等. 基于失真模型的结构相似度图像质量评价[J]. J4, 2009, 43(5): 864-868.

LOU Bin, CHEN Hai-Bin, DIAO Wu-Feng, et al. Structural similarity image quality assessment based on distortion model. J4, 2009, 43(5): 864-868.

链接本文:

http://www.zjujournals.com/xueshu/eng/CN/10.3785/j.issn.1008-973X.2009.05.015    或    http://www.zjujournals.com/xueshu/eng/CN/Y2009/V43/I5/864

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