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Quality assessment of screen content images based on multi-stage dictionary learning
Journal of Visual Communication and Image Representation ( IF 2.6 ) Pub Date : 2021-07-30 , DOI: 10.1016/j.jvcir.2021.103248
Yongli Chang 1 , Sumei Li 1 , Anqi Liu 1 , Jie Jin 1
Affiliation  

In this paper, we propose an effective method for quality assessment of screen content images (SCIs) based on multi-stage dictionary learning. To simulate the brain’s layered processing of signals, we proposed a hierarchical feature extraction strategy, which is called multi-stage dictionary learning, to simulate the hierarchical information processing of brain. First, the standard deviation of normalized map obtained from training image is used to select the training data in a certain proportion, which can ensure the learning efficiency and reduce the training burden. Next, the reconstructed map is weighted as the input of the next-stage dictionary learning. Then using the trained dictionary, the sparse representation is applied to extract features. Meanwhile, considering that some important features may be ignored in the process of multi-stage dictionary learning, we use Log Gabor filter to extract feature maps, and then calculate the correlation between feature maps as another kind of compensation features. Final, for the two feature sets, we choose SVR and feature codebook to learn two objective scores, and then use the adaptive weighting strategy to get the final objective quality score. Experimental results show that the proposed method is superior to several mainstream SCIs metrics on two publicly available databases.



中文翻译:

基于多阶段字典学习的屏幕内容图像质量评估

在本文中,我们提出了一种基于多阶段字典学习的屏幕内容图像(SCI)质量评估的有效方法。为了模拟大脑对信号的分层处理,我们提出了一种分层特征提取策略,称为多阶段字典学习,来模拟大脑的分层信息处理。首先,利用训练图像得到的归一化图的标准差,按一定比例选取训练数据,保证学习效率,减轻训练负担。接下来,重构的地图被加权作为下一阶段字典学习的输入。然后使用训练好的字典,应用稀疏表示来提取特征。同时,考虑到在多阶段字典学习过程中可能会忽略一些重要的特征,我们使用Log Gabor滤波器提取特征图,然后计算特征图之间的相关性作为另一种补偿特征。最后,对于这两个特征集,我们选择SVR和特征码本学习两个客观分数,然后使用自适应加权策略得到最终的客观质量分数。实验结果表明,所提出的方法优于两个公开可用数据库上的几个主流 SCI 指标。然后使用自适应加权策略得到最终的客观质量分数。实验结果表明,所提出的方法优于两个公开可用数据库上的几个主流 SCI 指标。然后使用自适应加权策略得到最终的客观质量分数。实验结果表明,所提出的方法优于两个公开可用数据库上的几个主流 SCI 指标。

更新日期:2021-08-11
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