基于多特征融合和卷积神经网络的无参考图像质量评价

卢鹏; 刘楷贇; 邹国良; 王振华; 郑宗生

doi:10.37188/CJLCD.2021-0175

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基于多特征融合和卷积神经网络的无参考图像质量评价

图像处理 | 更新时间：2022-01-19

- 基于多特征融合和卷积神经网络的无参考图像质量评价
- No reference image quality assessment based onfusion of multiple features and convolutional neural network
- 液晶与显示 2022年37卷第1期页码：66-76
- 作者机构：
  
  上海海洋大学信息学院, 上海 201306
- 作者简介：
  
  [ "卢鹏 (1981-), 男, 河南新乡人, 博士，教授, 2013年于上海海事大学获得博士学位，主要研究方向为人工智能、智能信息处理。E-mail: plu@shou.edu.cn" ]
- 基金信息：
  
  上海市科技创新计划(20dz203800);上海市地方能力建设项目(19050502100);上海海洋大学科技发展专项(A2-2006-20-200211)
- DOI：10.37188/CJLCD.2021-0175
  中图分类号： TP391.41
- 收稿日期：2021-07-02，
  
  修回日期：2021-09-06，
  
  纸质出版日期：2022-01
- 稿件说明：
移动端阅览
卢鹏, 刘楷贇, 邹国良, 等. 基于多特征融合和卷积神经网络的无参考图像质量评价[J]. 液晶与显示, 2022,37(1):66-76.

Peng LU, Kai-yun LIU, Guo-liang ZOU, et al. No reference image quality assessment based onfusion of multiple features and convolutional neural network[J]. Chinese journal of liquid crystals and displays, 2022, 37(1): 66-76.
卢鹏, 刘楷贇, 邹国良, 等. 基于多特征融合和卷积神经网络的无参考图像质量评价[J]. 液晶与显示, 2022,37(1):66-76. DOI： 10.37188/CJLCD.2021-0175.

Peng LU, Kai-yun LIU, Guo-liang ZOU, et al. No reference image quality assessment based onfusion of multiple features and convolutional neural network[J]. Chinese journal of liquid crystals and displays, 2022, 37(1): 66-76. DOI： 10.37188/CJLCD.2021-0175.

摘要

为了更好地评价图像质量，解决在基于卷积神经网络的图像质量评价模型(CNN-IQA)上明显忽略的分块图像之间存在差异性的问题，提出了一种多特征融合的CNN模型。首先，将整幅图像进行不重叠分块，并提取每个分块图像的信息熵和纹理特征。然后，将提取计算的两特征相结合，计算各分块图像的重要性权重，以此衡量分块图像对失真图像质量的影响。最后，根据计算出的重要性权重修改损失函数，突出重要性高的分块图像在训练过程中发挥的作用。在LIVE数据集进行验证和对比实验发现，该算法的SROCC与LCC指标为0.962和0.960，相比原算法至少提升0.9%；在TID2008数据集上验证和对比实验发现，该算法获得的SROCC与LCC指标为0.922和0.926，相比原算法至少提升0.6%。并且在两数据集上的结果均优于其他对比算法。实验结果证明了其在预测图像质量方面具有良好的性能和泛化性。

Abstract

In order to evaluate the image quality better and solve the problem of the differences between the block images that are obviously ignored in the image quality evaluation model based on the convolutional neural network (CNN-IQA)

a CNN model of multi-feature fusion is proposed. Firstly

the whole image is divided into non-overlapping blocks

the information entropy and texture feature of each divided image are extracted. Then

the two features are combined to calculate the importance weight of each block image to measure the influence of the block image on the quality of the distorted image. Finally

the loss function is modified according to the calculated importance weight to highlight the role of the block image with high importance in the training process. Validation and comparison experiments on the LIVE data set found that the SROCC and LCC indicators of the algorithm are 0.962 and 0.960

which are higher than the original algorithm at least 0.9%. The validation and comparison experiments on the TID2008 data set show that the SROCC and LCC indicators obtained by the algorithm are 0.922 and 0.924

which are higher than the original algorithm at least 0.6%. And the results on the two data sets are better than other comparison algorithms. The experimental results prove that it has good performance and generalization in predicting image quality.

关键词

Keywords

references

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