Blind image quality assessment for multiply distorted images via convolutional neural networks
Blind image quality assessment for multiply distorted images via convolutional neural networks
复制标题
DOI：
10.1109/icassp.2016.7471841
复制
发表时间：
2016-03
期刊：
影响因子：
--
通讯作者：
Jie Fu;Hanli Wang;L. Zuo
中科院分区：
文献类型：
--
作者：
Jie Fu;Hanli Wang;L. Zuo
在过去的十年里，图像质量评价（IQA）技术得到了不断的发展。然而，对于多失真类型的IQA的研究仍然有限，特别是在盲图像质量评估方法。本文提出了一种基于卷积神经网络（CNN）的方法来预测无参考的多重失真图像的质量。受早期人类视觉模型的启发，提出的基于CNN的方法结合了特征学习和回归来估计多重失真图像的质量。该网络由一个卷积层，一个具有最大和平均池化的池化层，两个全连接层和一个softmax分类层组成。利用这种网络结构，探索了CNN的准确性与IQA的预测单调性之间的关系。最新发布的LIVE多重失真图像质量数据库上的实验结果验证了所提出的基于CNN的方法的有效性。
The past decade has witnessed a growing development of Image Quality Assessment (IQA) techniques. However, the researches of IQA with multiple distortion types are still limited especially on blind image quality assessment methods. In this paper, a Convolutional Neural Network (CNN) based method is proposed to predict the quality of multiply distorted images without references. Inspired by the early human visual model, the proposed CNN based method combines feature learning and regression for estimating the quality of multiply distorted images. The proposed network consists of one convolutional layer, one pooling layer with max and average pooling, two full connection layers and one softmax classification layer. With this network structure, the relationship between the accuracy of CNN and the prediction monotonicity of IQA is explored. Experimental results on the newly released LIVE multiply distorted image quality database verify the effectiveness of the proposed CNN based method.