Regression-based blind image quality assessment (IQA) models are susceptible
to biased training samples, leading to a biased estimation of model parameters.
To mitigate this issue, we propose a regression-free framework for image
quality evaluation, which is founded upon retrieving similar instances by
incorporating semantic and distortion features. The motivation behind this
approach is rooted in the observation that the human visual system (HVS) has
analogous visual responses to semantically similar image contents degraded by
the same distortion. The proposed framework comprises two classification-based
modules: semantic-based classification (SC) module and distortion-based
classification (DC) module. Given a test image and an IQA database, the SC
module retrieves multiple pristine images based on semantic similarity. The DC
module then retrieves instances based on distortion similarity from the
distorted images that correspond to each retrieved pristine image. Finally, the
predicted quality score is derived by aggregating the subjective quality scores
of multiple retrieved instances. Experimental results on four benchmark
databases validate that the proposed model can remarkably outperform the
state-of-the-art regression-based models.

基于检索相似的图像实例来评估图像质量的无回归框架，包含语义分类模块和畸变分类模块，根据语义和畸变相似度检索多个图像实例并聚合主观质量评分，验证实验表明该模型显著优于基于回归的模型。