Blind image quality assessment (BIQA) plays an important role in image services as independent of the reference image. Herein, the perceptual relevant feature design is the core of BIQA methods, but their performance is still not satisfied at present. In this work, we propose an unsupervised feature extraction approach for BIQA based on Karhunen-Loéve transform (KLT). Specifically, a normalization operation is firstly applied to the test image by calculating its mean subtracted contrast normalized (MSCN) coefficient. Then, KLT is employed as a data-driven feature extraction approach to extract image structural features, wherein kernels with different sizes are utilized to perform multi-scale analysis. Finally, generalized Gaussian distribution (GGD) is employed to model the KLT coefficients distribution in different spectral components as quality relevant features. Extensive experiments conducted on four widely utilized IQA databases have demonstrated that the proposed Multi-scale KLT (MsKLT) BIQA metric compares favorably with existing BIQA methods in terms of high accordance with human subjective scores on both common and uncommon distortion types.

中文翻译：

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基于多尺度 KLT 的盲图像质量评估

盲图像质量评估 (BIQA) 在独立于参考图像的图像服务中发挥着重要作用。其中，感知相关特征设计是BIQA方法的核心，但其性能目前还不能令人满意。在这项工作中，我们提出了一种基于 Karhunen-Loéve 变换（KLT）的 BIQA 无监督特征提取方法。具体地，首先通过计算其平均减法对比度归一化(MSCN)系数对测试图像应用归一化操作。然后，采用 KLT 作为数据驱动的特征提取方法来提取图像结构特征，其中利用不同大小的内核进行多尺度分析。最后，广义高斯分布 (GGD) 用于将不同频谱分量中的 KLT 系数分布建模为质量相关特征。在四个广泛使用的 IQA 数据库上进行的大量实验表明，所提出的多尺度 KLT (MsKLT) BIQA 指标与现有的 BIQA 方法相比，在对常见和不常见失真类型的人类主观评分高度一致方面具有优势。