No-reference image quality assessment using joint color space statistical and texture feature

Ci-en FAN; Jie-wen RAN; Jia YAN; Lian ZOU; Wen-xuan SHI

doi:10.3788/OPE.20182604.0916

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No-reference image quality assessment using joint color space statistical and texture feature

Information Sciences | 更新时间：2020-07-05

- No-reference image quality assessment using joint color space statistical and texture feature
- Optics and Precision Engineering Vol. 26, Issue 4, Pages: 916-926(2018)
- 作者机构：
  
  1.武汉大学电子信息学院, 湖北武汉 430072
  2.武汉大学遥感信息工程学院, 湖北武汉 430072
- 作者简介：
- 基金信息：
- DOI：10.3788/OPE.20182604.0916
  CLC： TP391
- Received：04 September 2017，
  
  Accepted：2017-11-6，
  
  Published：25 April 2018
- 稿件说明：
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Ci-en FAN, Jie-wen RAN, Jia YAN, et al. No-reference image quality assessment using joint color space statistical and texture feature[J]. Optics and precision engineering, 2018, 26(4): 916-926.
DOI：

Ci-en FAN, Jie-wen RAN, Jia YAN, et al. No-reference image quality assessment using joint color space statistical and texture feature[J]. Optics and precision engineering, 2018, 26(4): 916-926. DOI： 10.3788/OPE.20182604.0916.

摘要

为了客观评价图像质量，本文提出联合颜色空间统计特征和权重局部二值模式（Local Binary Pattern，LBP）纹理特征的无参考图像质量评价模型。首先，对失真图像进行亮度去均值对比度归一化（Mean Subtracted Contrast Normalized，MSCN）操作得到MSCN系数；然后，对MSCN系数提取其统计参数特征和权重LBP直方图特征，其中统计参数由广义高斯模型获得，权重为MSCN系数的幅度。另外，还采用了

Lαβ

颜色空间下红绿和蓝黄分量的自然场景统计（Natural Scence Statistics，NSS）特征来增强基于颜色失真的描述，并运用非对称广义高斯模型获得统计参数特征。最后，运用SVR建立图像质量评价指标到主观质量得分的回归模型。在LIVE，CSIQ，TID2013和MLIVE数据库上的实验结果表明：4个数据库加权平均Spearman秩相关系数为0.776，Pearson线性相关系数为0.821，均优于其他方法；图像大小为512×512时特征提取只需0.19 s。本文提出的方法与人眼主观感知具有良好的一致性，并具有复杂度低等优点。

Abstract

In order to evaluate the image quality objectively

a no-reference image quality evaluation model

which combined color space statistical feature and weighted local binary model (LBP) texture features

was established in this paper. Firstly

the mean subtracted contrast normalized (MSCN) coefficients were obtained by applying MSCN to the luminance of the distorted image. Then

the statistical parameters and weighted LBP histogram features were extracted from the MSCN coefficients. The statistical parameters were obtained from the generalized Gaussian model and the weight was the magnitude of the MSCN coefficients. In addition

the natural scene statistics (NSS) feature of red-green and blue-yellow components in

Lαβ

color space was used to enhance the description based on color distortion

and the statistical feature parameters were obtained by using asymmetric generalized Gaussian model. Finally

SVR was used to establish the regression model that mapping image quality evaluation index to subjective quality score. The experimental results on LIVE

CSIQ

TID2013 and MLIVE databases showed that the weighted average Spearman rank correlation coefficient (SROCC) was 0.776 and the Pearson linear correlation coefficient (PLCC) was 0.821

which was superior to other methods. In addition

the extraction of 512×512 size image feature spent only 0.19 s. The method proposed in this paper is consistent with the subjective perception of human eyes and has the advantage of low complexity.

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Keywords

references

王志明.无参考图像质量评价综述[J].自动化学报, 2015, 41(6):1062-1079.

WANG ZH M. Review of no-reference image quality assessment[J]. Acta Automatica Sinica, 2015, 41(6):1062-1079. (in Chinese)

MANAP R A, SHAO L. Non-distortion-specific no-reference image quality assessment:a survey[J]. Information Sciences, 2015, 301:141-160.

ZHU T, KARAM L. A no-reference objective image quality metric based on perceptually weighted local noise[J]. EURASIP Journal on Image and Video Processing, 2014, 2014:5.

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.

NARVEKAR N D, KARAM L J. A no-reference image blur metric based on the cumulative probability of blur detection (CPBD)[J]. IEEE Transactions on Image Processing, 2011, 20(9):2679-2683.

FANG Y M, MA K D, WANG ZH, et al.. No-reference quality assessment of contrast-distorted images based on natural scene statistics[J]. IEEE Signal Processing Letters, 2015, 22(7):838-843.

MOORTHY A K, BOVIK A C. A two-step framework for constructing blind image quality indices[J]. IEEE Signal Processing Letters, 2010, 17(5):513-516.

SAAD MA, BOVIK A C, CHARRIER C. A DCT Statistics-based blind image quality index[J]. IEEE Signal Processing Letters, 2010, 17(6):583-586.

SAAD M A, BOVIK A C, CHARRIER C. DCT statistics model-based blind image quality assessment[C]. Proceedings of the 18 th IEEE International Conference on Image Processing , IEEE , 2011: 3093-3096.

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.

MITTAL A, MOORTHY A K, BOVIK A C. No-reference image quality assessment in the spatial domain[J]. IEEE Transactions on Image Processing, 2012, 21(12):4695-4708.

张闯, 王亚明, 陈苏婷.基于空间依存的无参考图像质量评价[J].光学精密工程, 2015, 23(11):3211-3218.

ZHANG CH, WANG Y M, CHEN S T. No-reference image quality assessment based on spatial dependency[J]. Opt. Precision Eng., 2015, 23(11):3211-3218. (in Chinese)

XUE W F, MOU X Q, ZHANG L, et al.. Blind image quality assessment using joint statistics of gradient magnitude and Laplacian features[J]. IEEE Transactions on Image Processing, 2014, 23(11):4850-4862.

ZHANG M, MURAMATSU C, ZHOU X, et al.. Blind image quality assessment using the joint statistics of generalized local Binary pattern[J]. IEEE Signal Processing Letters, 2015, 22(2):207-210.

GHADIYARAM D, BOVIK A C. Perceptual quality prediction on authentically distorted images using a bag of features approach[J]. Journal of Vision, 2017, 17(1):32.

YE P, KUMAR J, KANG L, et al. . Unsupervised feature learning framework for no-reference image quality assessment[C] . Proceedings of 2012 IEEE Conference on Computer Vision and Pattern Recognition , IEEE , 2012: 1098-1105.

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

ZHANG L, ZHANG L, BOVIK A C. A feature-enriched completely blind image quality Evaluator[J]. IEEE Transactions on Image Processing, 2015, 24(8):2579-2591.

RUDERMAN D L. The statistics of natural images[J]. Network:Computation in Neural Systems, 1994, 5(4):517-548.

刘丽, 谢毓湘, 魏迎梅, 等.局部二进制模式方法综述[J].中国图象图形学报, 2014, 19(12):1696-1720.

LIU L, XIE Y X, WEI Y M, et al.. Survey of local binary pattern method[J]. Journal of Image and Graphics, 2014, 19(12):1696-1720. (in Chinese)

RUDERMAN D L, CRONIN T W, CHIAO C C. Statistics of cone responses to natural images:implications for visual coding[J]. Journal of the Optical Society of America A, 1998, 15(8):2036-2045.

LI Q H, LIN W S, XU J T, et al.. Blind image quality assessment using statistical structural and luminance features[J]. IEEE Transactions on Multimedia, 2016, 18(12):2457-2469.

刘建磊.结合局部特征的无参考彩色图像质量评价[J].光学精密工程, 2016, 24(5):1176-1184.

LIU J L. No-reference color image quality assessment based on local features[J]. Opt. Precision Eng., 2016, 24(5):1176-1184. (in Chinese)

LI Q H, LIN W S, FANG Y M. No-reference quality assessment for multiply-distorted images in gradient domain[J]. IEEE Signal Processing Letters, 2016, 23(4):541-545.

CHANG C C, LIN C J. LIBSVM: a library for support vector machines[EB/OL]. (2016-12-22). http://www.csie.ntu.edu.tw/~cjlin/libsvm/index.html http://www.csie.ntu.edu.tw/~cjlin/libsvm/index.html .

卢彦飞, 张涛.基于Riesz变换的结构相似度图像质量评价方法[J].液晶与显示, 2015, 30(6):992-999.

LU Y F, ZHANG T. Image quality assessment method via Riesz-transform based structural similarity[J]. Chinese Journal of Liquid Crystals and Displays, 2015, 30(6):992-999. (in Chinese)

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