陈勇^1,,,
朱凯欣¹,
房昊¹,
刘焕淋²
1.重庆邮电大学工业物联网与网络化控制教育部重点实验室 重庆 400065
2.重庆邮电大学通信与信息工程学院 重庆 400065
基金项目:国家自然科学基金(51977021)

详细信息

作者简介:陈勇：男，1963年生，博士，教授，研究方向为图像处理
朱凯欣：女，1994年，硕士生，研究方向为无参考图像质量评价和立体图像质量评价
房昊：男，1993年，硕士，研究方向为无参考图像质量评价
刘焕淋：女，1970年生，博士，教授，研究方向为信号处理等方面

通讯作者:陈勇　chenyong@cqupt.edu.cn

中图分类号:TN911.73; TP391.41

计量

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被引次数:0

出版历程

收稿日期:2019-09-17
修回日期:2020-02-16
网络出版日期:2020-03-09
刊出日期:2020-10-13

No-reference Image Quality Evaluation for Multiply-distorted Images Based on Spatial Domain Coding

Yong CHEN^1,,,
Kaixin ZHU¹,
Hao FANG¹,
Huanlin LIU²
1. Key Laboratory of Industrial Internet of Things & Network Control, Ministry of Education, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
2. School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
Funds:The National Natural Science Foundation of China (51977021)


摘要
摘要:针对难以准确有效地提取混合失真图像质量特征的问题，该文提出一种基于空间分布分析的图像质量评价方法。首先将图像进行亮度系数归一化处理，然后将图像进行分块，利用卷积神经网络(CNN)进行端对端的深度学习，采用多层次卷积核堆叠的方法获取图像的质量感知特征，并通过全连接层将特征映射到图像块的质量分数。再将块质量分数汇总获取质量池，通过对质量池中局部质量的空间分布情况进行分析，提取能够表征其空间分布情况的特征，然后采用神经网络建立局部质量到整体质量的映射模型，将图像的局部质量进行汇总。最后在MLIVE, MDID2013, MDID2016混合失真图像库中进行性能测试以及与相关的对比算法进行比较，验证了该算法的有效性。
关键词:图像质量评价/
无参考/
卷积神经网络
Abstract:Considering the problem that it is difficult to accurately and effectively extract the quality features of mixed distortion image, an image quality assessment method based on spatial distribution analysis is proposed. Firstly, the brightness coefficients of the image are normalized, and the image is divided into blocks. While the Convolutional Neural Network (CNN) is used for end-to-end depth learning, the multi-level stacking of convolution cores is applied to acquire image quality perception features. The feature is mapped to the mass fraction of the image block through the full connection layer, then the quality pool is obtained by aggregating the quality of the block. Through the analysis of the spatial distribution of local quality in the quality pool, the features that can represent its spatial distribution are extracted, and then the mapping model from local quality to overall quality is established by the neural network to aggregate the local quality of the image. Finally, the effectiveness of the algorithm is verified by the performance tests in MLIVE, MDID2013 and MDID2016 mixed distortion image databases.
Key words:Image quality assessment/
No-reference/
Convolutional Neural Network (CNN)



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