Blind Quality Assessment of Screen Content Images Via Macro-Micro Modeling of Tensor Domain Dictionary
Blind Quality Assessment of Screen Content Images Via Macro-Micro Modeling of Tensor Domain Dictionary
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DOI：
10.1109/tmm.2020.3039382
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发表时间：
2021
影响因子：
7.3
通讯作者：
Yongqiang Bai;Zhongjie Zhu;G. Jiang;Hui Sun
中科院分区：
文献类型：
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作者：
Yongqiang Bai;Zhongjie Zhu;G. Jiang;Hui Sun
屏幕内容图像（Screen content images，SCI）在交互式多媒体应用中得到了迅速而广泛的应用。SCI的质量评估问题是一个有趣的研究课题。现有的图像质量预测方法大多采用主观独立的灰度域特征进行预测，不能全面表征图像特性或缺乏统一的数学解释。针对这些问题，本文提出了一种基于张量域字典宏微观建模的SCI盲质量评估方法。在该方法中，首先探索张量分解，以避免颜色信息的损失，然后与主成分更有效地学习目标字典。在目标字典空间中建立宏-微观模型来表征微观和宏观特征，为特征提取提供了系统的数学解释。对于微观特征，通过分析稀疏码统计分布的特殊性，设计了一种对数正态池化方案来提高特征聚合的有效性。此外，基于伯努利大数定律，重点讨论和研究了SCI的统计特性，并生成了一个可靠的宏观特征来描述SCI的统计分布与质量退化之间的关系。实验结果表明，该方法在预测SCI图像的视觉质量方面优于现有的相关方法，特别是在对畸变类型的泛化能力和特征生成的可解释性方面。
Screen content images (SCIs) have been rapidly and widely applied in interactive multimedia applications. The problem of quality assessment for SCIs is an interesting research topic. Most of the existing methods use subjective and independent features in gray domain to predict the image quality, which cannot comprehensively characterize the image properties or lack unified mathematical explanation for SCIs. To address these problems, we propose a novel blind quality assessment method based on macro-micro modeling of tensor domain dictionary for SCIs in this article. In the proposed method, the tensor decomposition is explored first to avoid the loss of color information, and then a target dictionary is learned more effectively with the principal components. Furthermore, a macro-micro model is established to characterize the micro and macro features in the target dictionary space, which can provide a systematic mathematical interpretation for feature extraction. For the micro features, a log-normal pooling scheme is designed to enhance the effectiveness of feature aggregation by analyzing the particularity of the statistical distribution of sparse codes. Additionally, the statistical properties are mainly discussed and studied based on the Bernoulli law of large numbers, and then a reliable macro feature is generated to describe the relationship between the statistical distribution and quality degradation of SCIs. Experimental results determined by using three public SCI databases show that the proposed method can perform better than relevant existing methods in the prediction of the visual quality of SCIs, especially in terms of the generalization for distortion type and interpretability for feature generation.