Deep learning-based methods have significantly influenced the blind image quality assessment (BIQA) field, however, these methods often require training using large amounts of human rating data. In contrast, traditional knowledge-based methods are cost-effective for training but face challenges in effectively extracting features aligned with human visual perception. To bridge these gaps, we propose integrating deep features from pre-trained visual models with a statistical analysis model into a Multi-scale Deep Feature Statistics (MDFS) model for achieving opinion-unaware BIQA (OU-BIQA), thereby eliminating the reliance on human rating data and significantly improving training efficiency. Specifically, we extract patch-wise multi-scale features from pre-trained vision models, which are subsequently fitted into a multivariate Gaussian (MVG) model. The final quality score is determined by quantifying the distance between the MVG model derived from the test image and the benchmark MVG model derived from the high-quality image set. A comprehensive series of experiments conducted on various datasets show that our proposed model exhibits superior consistency with human visual perception compared to state-of-the-art BIQA models. Furthermore, it shows improved generalizability across diverse target-specific BIQA tasks. Our code is available at: https://github.com/eezkni/MDFS

翻译：基于深度学习的方法已显著影响盲图像质量评估领域，然而这些方法通常需要利用大量人工评分数据进行训练。相比之下，基于传统知识的训练方法虽具有成本效益，但在有效提取符合人类视觉感知的特征方面面临挑战。为弥合这些差距，我们提出将预训练视觉模型的深度特征与统计分析模型相结合，构建多尺度深度特征统计模型，以实现无主观盲图像质量评估，从而消除对人工评分数据的依赖并显著提升训练效率。具体而言，我们从预训练视觉模型中提取基于图像块的多尺度特征，随后将其拟合到多元高斯模型中。最终质量分数通过量化测试图像衍生的多元高斯模型与高质量图像集衍生的基准多元高斯模型之间的距离来确定。在多个数据集上进行的一系列综合实验表明，相较于当前最先进的盲图像质量评估模型，我们提出的模型展现出与人类视觉感知更优的一致性。此外，该模型在不同目标特定的盲图像质量评估任务中表现出更强的泛化能力。我们的代码公开于：https://github.com/eezkni/MDFS