颜色空间统计联合纹理特征的无参考图像质量评价

范赐恩; 冉杰文; 颜佳; 邹炼; 石文轩

doi:10.3788/OPE.20182604.0916

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颜色空间统计联合纹理特征的无参考图像质量评价

信息科学 | 更新时间：2020-07-05

- 颜色空间统计联合纹理特征的无参考图像质量评价
- No-reference image quality assessment using joint color space statistical and texture feature
- 光学精密工程 2018年26卷第4期页码：916-926
- 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：
  
  [ "范赐恩(1975-), 女, 浙江慈溪人, 博士, 副教授, 2012年于武汉大学获得博士学位, 主要从事图像处理、计算机视觉等方面研究。E-mail: fce@whu.edu.cn" ]
  [ "冉杰文(1993-), 男, 四川平昌人, 硕士研究生, 2016年于华中师范大学获得学士学位, 主要从事图像质量评价、计算机视觉等方面研究。E-mail:jiewenran@whu.edu.cn" ]
- 基金信息：
  
  国家自然科学基金资助项目(61701351)
- DOI：10.3788/OPE.20182604.0916
  中图分类号： TP391
- 收稿日期：2017-09-04，
  
  录用日期：2017-11-6，
  
  纸质出版日期：2018-04-25
- 稿件说明：
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范赐恩, 冉杰文, 颜佳, 等. 颜色空间统计联合纹理特征的无参考图像质量评价[J]. 光学精密工程, 2018,26(4):916-926.

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.
范赐恩, 冉杰文, 颜佳, 等. 颜色空间统计联合纹理特征的无参考图像质量评价[J]. 光学精密工程, 2018,26(4):916-926. DOI： 10.3788/OPE.20182604.0916.

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.

关键词

Keywords

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