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Blind image quality assessment in the contourlet domain
Signal Processing: Image Communication ( IF 3.5 ) Pub Date : 2020-11-07 , DOI: 10.1016/j.image.2020.116064
Chaofeng Li , Tuxin Guan , Yuhui Zheng , Xiaochun Zhong , Xiaojun Wu , Alan Bovik

No-reference/blind image quality assessment (NR-IQA/BIQA) algorithms play an important role in image evaluation, as they can assess the quality of an image automatically, only using the distorted image whose quality is being assessed. Among the existing NR-IQA/BIQA methods, natural scene statistic (NSS) models which can be expressed in different bandpass domains show good consistency with human subjective judgments of quality.

In this paper, we create new ‘quality-aware’ features: the energy differences of the sub-band coefficients across scales via contourlet transform, and propose a new NR-IQA/BIQA model that operates on natural scene statistics in the contourlet domain. Prior to applying the contourlet transform, we apply two preprocessing steps that help to create more information-dense, low-entropy representations. Specifically, we transform the picture into the CIELAB color space and gradient magnitude map. Then, a number of ‘quality-aware’ features are discovered in the contourlet transform domain: the energy of the sub-band coefficients within scales, and the energy differences between scales, as well as measurements of the statistical relationships of pixels across scales. A detailed analysis is conducted to show how different distortions affect the statistical characteristics of these features, and then features are fed to a support vector regression (SVR) model which learns to predict image quality. Experimental results show that the proposed method has high linearity against human subjective perception, and outperforms the state-of-the-art NR-IQA models.



中文翻译:

Contourlet域的盲像质量评估

无参考/盲图像质量评估(NR-IQA / BIQA)算法在图像评估中起着重要作用,因为它们可以仅使用要评估其质量的失真图像自动评估图像的质量。在现有的NR-IQA / BIQA方法中,可以在不同带通域中表达的自然场景统计(NSS)模型显示出与人类主观质量判断的良好一致性。

在本文中,我们创建了新的“质量感知”功能:通过Contourlet变换跨尺度的子带系数的能量差,并提出了一个新的NR-IQA / BIQA模型,该模型在Contourlet域中的自然场景统计上运行。在应用Contourlet变换之前,我们应用两个预处理步骤,以帮助创建更多的信息密集,低熵表示。具体来说,我们将图片转换为CIELAB颜色空间和渐变幅度图。然后,在Contourlet变换域中发现了许多“质量意识”特征:标度内子带系数的能量,标度之间的能量差以及标度在标度之间的统计关系的度量。进行了详细的分析,以显示不同的失真如何影响这些特征的统计特征,然后将特征馈入支持向量回归(SVR)模型,该模型学习预测图像质量。实验结果表明,所提出的方法对人类主观感知具有很高的线性度,并且优于最新的NR-IQA模型。

更新日期:2020-11-19
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