No Reference Quality Assessment for Multiply-Distorted Images Based on an Improved Bag-of-Words Model
No Reference Quality Assessment for Multiply-Distorted Images Based on an Improved Bag-of-Words Model
复制标题
基于改进词袋模型的多重畸变图像无参考质量评估
DOI：
10.1109/lsp.2015.2436908
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发表时间：
2015-05
影响因子：
3.9
通讯作者：
Tao Dacheng
中科院分区：
文献类型：
--
作者：
Lu Yanan;Xie Fengying;Liu Tongliang;Jiang Zhiguo;Tao Dacheng
多重失真评估是图像质量评估中的一大难题。本文提出了一种用于多重畸变图像的无参考IQA模型。首先从三种NSS特征中选择对每种失真类型都敏感的特征，即使存在其他失真。然后应用改进的词袋模型对所选特征进行编码。最后，使用简单而有效的线性组合将图像特征映射到质量分数。组合权值通过套索回归得到。一系列实验表明，该特征选择策略和改进的BoW模型能够有效地提高多失真IQA的质量预测精度。与其他算法相比，该方法在多失真IQA中具有较好的效果。
Multiple distortion assessment is a big challenge in image quality assessment (IQA). In this letter, a no reference IQA model for multiply-distorted images is proposed. The features, which are sensitive to each distortion type even in the presence of other distortions, are first selected from three kinds of NSS features. An improved Bag-of-Words (BoW) model is then applied to encode the selected features. Lastly, a simple yet effective linear combination is used to map the image features to the quality score. The combination weights are obtained through lasso regression. A series of experiments show that the feature selection strategy and the improved BoW model are effective in improving the accuracy of quality prediction for multiple distortion IQA. Compared with other algorithms, the proposed method delivers the best result for multiple distortion IQA.
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DOI：
10.1145/3424978.3425111
发表时间：
2020-10
期刊：
Proceedings of the 4th International Conference on Computer Science and Application Engineering
影响因子：
--
作者：
Lihao Zheng;Genyuan Zhang;Yaping Cai
通讯作者：
Lihao Zheng;Genyuan Zhang;Yaping Cai
DOI：
10.1109/cvpr.2005.16
发表时间：
2005-06
期刊：
2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05)
影响因子：
--
作者：
Li Fei-Fei-Li-Fei-Fei-48004138;P. Perona
通讯作者：
Li Fei-Fei-Li-Fei-Fei-48004138;P. Perona
DOI：
--
发表时间：
2004
期刊：
--
影响因子：
--
作者：
P. Callet;F. Autrusseau
通讯作者：
P. Callet;F. Autrusseau
DOI：
10.1109/cvpr.2013.133
发表时间：
2013-06
期刊：
2013 IEEE Conference on Computer Vision and Pattern Recognition
影响因子：
--
作者：
Wufeng Xue;Lei Zhang;X. Mou
通讯作者：
Wufeng Xue;Lei Zhang;X. Mou
影响因子：
2.9
作者：
Jiansheng Qian;Dong Wu;Leida Li;Deqiang Cheng;Xuesong Wang
通讯作者：
Xuesong Wang