基于生物视觉标准模型特征的无参考型图像质量评价方法

杨亚威; 李俊山; 张士杰; 芦鸿雁; 胡双演

doi:10.3788/YJYXS20142906.1016

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基于生物视觉标准模型特征的无参考型图像质量评价方法

成像技术与图像处理 | 更新时间：2020-08-12

- 基于生物视觉标准模型特征的无参考型图像质量评价方法
- Non-reference image quality assessment approach based on standard model features of biological vision
- 液晶与显示 2014年29卷第6期页码：1016-1023
- 作者机构：
  
  第二炮兵工程大学信息工程系, 陕西西安 710025
- 作者简介：
- 基金信息：
  
  国家自然科学基金（No.61175120）()
- DOI：10.3788/YJYXS20142906.1016
  中图分类号： TP394.1
- 收稿日期：2014-03-17，
  
  修回日期：2014-04-20，
  
  纸质出版日期：2014-12-05
- 稿件说明：
移动端阅览
杨亚威, 李俊山, 张士杰, 芦鸿雁, 胡双演. 基于生物视觉标准模型特征的无参考型图像质量评价方法[J]. 液晶与显示, 2014,29(6): 1016-1023

YANG Ya-wei, LI Jun-shan, ZHANG Shi-jie, LU Hong-yan, HU Shuang-yan. Non-reference image quality assessment approach based on standard model features of biological vision[J]. , 2014,29(6): 1016-1023
杨亚威, 李俊山, 张士杰, 芦鸿雁, 胡双演. 基于生物视觉标准模型特征的无参考型图像质量评价方法[J]. 液晶与显示, 2014,29(6): 1016-1023 DOI： 10.3788/YJYXS20142906.1016.

YANG Ya-wei, LI Jun-shan, ZHANG Shi-jie, LU Hong-yan, HU Shuang-yan. Non-reference image quality assessment approach based on standard model features of biological vision[J]. , 2014,29(6): 1016-1023 DOI： 10.3788/YJYXS20142906.1016.

摘要

鉴于生物视觉特征对于图像的良好表征能力，提出了一种基于生物视觉特征的无参考型图像质量评价方法。对生物视觉ST模型进行了研究和分析，完成了对图像的稀疏化表示；利用最小二乘支持向量机回归方法训练生物视觉特征到图像质量的映射关系，获得能够预测图像质量的回归器；通过学习的回归器完成了对图像质量的评价。基于LIVE图像库的实验结果表明，该方法对于特定失真和交叉失真的预测误差分别为2%和5%左右，并且与目前技术条件下的质量评价方法相比具有很好的精确性和单调性。

Abstract

As the biological vision features show superior performance to images representation

a non-reference image quality assessment approach based on biological vision features is proposed. The standard model of biological vision is studied and analyzed

and the sparse representation of image is accomplished through the model.The mapping correlation between biological vision features and image quality scores is trained with the regression technique of least square-support vector machine

which gains the regressor that can predict the image quality.The score of image quality assessment is accomplished with the trained regressor at last. The experimental results based on LIVE database show that the proposed approach has predicting error of 2% and 5% for specific distortion and cross-validation distortion respectively

and exhibits a superior accuracy and monotonicity compared to state-of-the-art quality assessment approaches.

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references

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