采用形状一致性特征的盲图像质量评价方法

储颖 1; 2; 牟轩沁 1; 洪伟 1

doi:10.7652/xjtuxb201408003

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采用形状一致性特征的盲图像质量评价方法

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- 采用形状一致性特征的盲图像质量评价方法
- A Blind Quality Assessment Method for Images Using Shape Consistency Feature
- 西安交通大学学报 2014年48卷第8期页码：12-17
- 作者机构：
  
  1. 西安交通大学电子与信息工程学院,西安,710049
  2. 深圳大学计算机与软件学院,广东,深圳,518060
- 作者简介：
- 基金信息：
  
  : 2013-12-18。作者简介: 储颖(1978—),女,博士生;牟轩沁(通信作者),男,教授,博士生导师。基金项目: 国家自然科学基金资助项目(90920003);国家自然科学基金委员会-广东省人民政府自然科学联合基金重点资助项目(U1201256);深圳市科技研发资金基础研究计划资助项目(JC201105160492A)
- DOI：10.7652/xjtuxb201408003
  中图分类号： TN911.73
- 网络首发：2014-08-10，
  
  纸质出版：2014
- 稿件说明：
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储颖 1, 2, 牟轩沁 1, 等. 采用形状一致性特征的盲图像质量评价方法[J]. 西安交通大学学报, 2014,48(8):12-17.

A Blind Quality Assessment Method for Images Using Shape Consistency Feature[J]. 2014, 48(8): 12-17.
储颖 1, 2, 牟轩沁 1, 等. 采用形状一致性特征的盲图像质量评价方法[J]. 西安交通大学学报, 2014,48(8):12-17. DOI： 10.7652/xjtuxb201408003.

A Blind Quality Assessment Method for Images Using Shape Consistency Feature[J]. 2014, 48(8): 12-17. DOI： 10.7652/xjtuxb201408003.

摘要

针对基于学习的盲图像质量评价方法评估性能易受训练样本库内容和学习策略影响的问题

提出一种无需训练学习的、采用条件直方图形状一致性特征的盲图像质量评价(SCCH)方法。该方法首先计算失真图像中相邻区分归一化变换系数的联合条件直方图

从中提取形状一致性特征; 接着依尺度分解特征矢量

并利用公开数据库构造特征属性-主观评分字典; 最后将特征子矢量一范数与字典中各特征属性比较排序

对字典中主观得分进行插值以计算失真图像质量评分。在两大公开数据库的实验结果表明

SCCH方法与图像质量主观评分的线性相关系数值大于0.82

稳定保持在较高水平。与传统盲图像质量评价方法相比

SCCH方法无需训练学习

质量评分公式形式简单

质量评价系统容易实现。

Abstract

A new blind quality assessment method for images is proposed to solve the problem that the evaluation performances of learning based blind image quality assessment(BIQA)methods are sensitive to the contents of training samples and learning strategies. The method uses the shape consistency of conditional histogram based BIQA metric(SCCH)and does not need training and learning. The method calculates the joint conditional histograms of neighboring divisive normalization transform coefficients in distorted images

and then extracts shape consistency features from the histograms. Then the feature vectors are decomposed by scale

and a feature characteristic-subjective score dictionary is constructed by using public database. The lengths of the extracted features in the dictionary are compared with that of the distorted image and are sorted

and an interpolation using the subjective scores in the dictionary is then performed to calculate the quality score of the distorted image. Experimental results in two public databases show that the linear correlation coefficient between the SCCH and the image quality subjective scores of distorted images is more than 82%

and maintains a relatively high level. Compared with traditional BIQA methods

the SCCH has the following features

it does not need training

its quality score formula is simple

and the quality assessment system is easy to implement.

关键词

Keywords

references

WANG Zhou. Applications of objective image quality assessment methods[J]. IEEE Signal Processing Magazine, 2011, 28(6): 137-142.

ZHU X, MILANFAR P. Automatic parameter selection for denoising algorithms using a no-reference measure of image content[J]. IEEE Transactions on Image Processing, 2010, 19(12): 3116-3132.

MOORTHY A K, BOVIK A C. Blind image quality assessment: from natural scene statistics to perceptual quality[J]. IEEE Transactions on Image Processing, 2011, 20(12): 3350-3364.

SHEIKH H R, BOVIK A C, CORMACK L. No-reference quality assessment using natural scene statistics: JPEG2000[J]. IEEE Transactions on Image Processing, 2005, 14(11): 1918-1927.

楼斌, 沈海斌, 赵武锋, 等. 基于自然图像统计的无参考图像质量评价[J]. 浙江大学学报: 工学版, 2010, 44(2): 248-252.

LOU Bin, SHEN Haibin, ZHAO Wufeng, et al. No-reference image quality assessment based on statistical model of natural image[J]. Journal of Zhejiang University: Engineering Science, 2010, 44(2): 248-252.

李朝锋, 唐国凤, 吴小俊, 等. 学习相位一致特征的无参考图像质量评价[J]. 电子与信息学报, 2013, 35(2): 484-488.

LI Chaofeng, TANG Guofeng, WU Xiaojun, et al. No-reference image quality assessment with learning phase congruency feature[J]. Journal of Electronics Information Technology, 2013, 35(2): 484-488.

CHU Ying, MOU Xuanqin, HONG Wei, et al. A novel blind image quality assessment metric and its feature selection strategy[C]∥Proceedings of the 2013 IST/SPIE Symposium on Electronic Imaging. San Francisco, CA, USA: SPIE, 2013: 1-8.

蒋刚毅, 黄大江, 王旭, 等. 图像质量评价方法研究进展[J]. 电子与信息学报, 2010, 32(1): 219-226.

JIANG Gangyi, HUANG Dajiang, WANG Xu, et al. Overview on image quality assessment methods[J]. Journal of Electronics Information Technology, 2010, 32(1): 219-226.

MITTAL A, MURALIDHAR G S, GHOSH J, et al. Blind image quality assessment without human training using latent quality factors[J]. IEEE Signal Processing Letters, 2012, 19(2): 75-78.

XUE Wufeng, ZHANG Lei, MOU Xuanqin. Learning without human scores for blind image quality assessment[C]∥Proceedings of the 2013 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway, NJ, USA: IEEE, 2013: 1-8.

LI Chaofeng, JU Yiwen, BOVIK A C, et al. No-training, no-reference image quality index using perceptual features[J]. Optical Engineering, 2013, 52(5): 057003. 1-6.

MITTAL A, SOUNDARARAJAN R, BOVIK A C. Making a ‘completely blind' image quality analyzer[J]. IEEE Signal Processing Letters, 2013, 20(3): 209-212.

BARLOW H B. Possible principles underlying the transformation of sensory message[M]∥ Sensory Communication. Cambridge, MA, USA: MIT Press, 1961: 217-234.

SCHWARTZ O, SIMONCELLI E P. Natural signal statistics and sensory gain control[J]. Nature Neuroscience, 2001, 4(8): 819-825.

WAINWRIGHT M J, SIMONCELLI E P. Scale mixtures of Gaussians and the statistics of natural images[J]. Advances in Neural Information Processing Systems, 2000, 12(1): 855-861.

SHEIKH H R, WANG Zhou, CORMACK L, et al. LIVE image quality assessment database release 2[EB/OL].[2013-11-08]. http: ∥live. ece. utexas. edu/research/quality/release2/databaserelease2.zip.

LARSON E C, CHANDLER D M. Most apparent distortion: full-reference image quality assessment and the role of strategy[J]. Journal of Electronic Imaging, 2010, 19(1): 011006. 1-21.

周秦武,隋芳芳,白平,等.嵌入式无接触视频心率检测方法.2013,47(12):55-60.[doi:10.7652/xjtuxb201312010]

郝红侠,刘芳,焦李成,等.采用结构自适应窗的非局部均值图像去噪算法.2013,47(12):71-76.[doi:10.7652/xjtuxb 201312013]

穆为磊,高建民,王昭,等.考虑人眼视觉特性的射线检测数字图像质量评价方法.2013,47(7):91-95.[doi:10.7652/xjtuxb201307017]

李玉花,齐春.利用位置字典对的人脸图像超分辨率方法.2012,46(6):7-11.[doi:10.7652/xjtuxb201206002]

穆为磊,高建民,陈富民,等.符合人眼视觉特性的焊缝射线数字图像增强方法.2012,46(3):90-93.[doi:10.7652/xjtuxb 201203016]

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