Opinion-unaware blind image quality assessment (OU BIQA) refers to establishing a blind quality prediction model without using the expensive subjective quality scores, which is a highly promising direction in the BIQA research. In this article, we focus on OU BIQA and propose a novel OU BIQA method. Specifically, in our proposed method, we deeply investigate the natural scene statistics (NSS) and the perceptual characteristics of the human brain for visual perception. Accordingly, a set of quality-aware NSS and perceptual characteristics-related features are designed to characterize the image quality effectively. For inferring the image quality, we learn a pristine multivariate Gaussian (MVG) model on a collection of pristine images, which serves as the reference information for quality evaluation. At last, the quality of a new given image is defined by measuring the divergence between its MVG model and the learned pristine MVG model. Thorough experiments performed on seven popular image databases demonstrate that the proposed OU BIQA method delivers superior performance to the state-of-the-art OU BIQA methods. The Matlab source code of the proposed method will be made publicly available at https://github.com/YT2015?tab=;repositories.

中文翻译：

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通过自然场景统计和感知特征进行盲图像质量评估

Opinion-unaware盲图像质量评估（OU BIQA）是指在不使用昂贵的主观质量分数的情况下建立盲质量预测模型，是BIQA研究中非常有前景的方向。在本文中，我们关注 OU BIQA，并提出了一种新颖的 OU BIQA 方法。具体来说，在我们提出的方法中，我们深入研究了自然场景统计（NSS）和人脑的视觉感知感知特征。因此，设计了一组质量感知 NSS 和感知特征相关的特征来有效地表征图像质量。为了推断图像质量，我们在一组原始图像上学习原始多元高斯 (MVG) 模型，作为质量评估的参考信息。最后，新给定图像的质量是通过测量其 MVG 模型和学习的原始 MVG 模型之间的差异来定义的。在七个流行的图像数据库上进行的彻底实验表明，所提出的 OU BIQA 方法比最先进的 OU BIQA 方法具有卓越的性能。所提议方法的 Matlab 源代码将在 https://github.com/YT2015?tab=;repositories 上公开。