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Quality assessment of screen content images based on multi-stage dictionary learning

机译：基于多级词典学习的屏幕内容图像质量评估

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摘要

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.
机译：在本文中，我们提出了一种基于多级词典学习的屏幕内容图像（SCI）的有效方法。为了模拟大脑的信号分层处理，我们提出了一种分层特征提取策略，称为多级字典学习，以模拟大脑的分层信息处理。首先，从训练图像获得的标准化图的标准偏差用于以一定比例选择培训数据，这可以确保学习效率并降低培训负担。接下来，重建的映射被加权作为下一个店本学习的输入。然后使用培训的字典，应用稀疏表示来提取特征。同时，考虑到在多级词典学习过程中可能忽略一些重要特征，我们使用Log Gabor滤波器提取特征映射，然后计算特征映射之间的相关性作为另一种补偿特征。 Final，对于两个功能集，我们选择SVR和特征码本来学习两个目标分数，然后使用自适应加权策略获得最终的客观质量分数。实验结果表明，该方法优于两个公开数据库的多个主流SCIS指标。

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