Learning deep vector regression model for no-reference image quality assessment

Jie Gu, Gaofeng Meng, Lingfeng Wang, Chunhong Pan

Abstract: The goal of no-reference image quality assessment (NR-IQA) is to estimate human perceived image quality without access to either reference image or prior knowledge about distortion type. Previous approaches for this problem are typically based on a regression framework that maps the image features directly to a quality score. In contrast, psychological evidence shows that humans prefer to evaluate visual quality with qualitative descriptions, e.g., using a five-grade ordinal scale: “excellent”, “good”, “fair”, “poor” and “bad”. Based on this observation, we propose a vector regression model that predicts five belief scores rather than a single quality score. The belief scores are designed to indicate the confidences of the test image being assigned with these five quality grades. In addition, with the purpose of more extensive applications, a saliency-based pooling strategy is presented to convert the predicted confidences into objective quality scores. Extensive experiments performed on two benchmark datasets demonstrate that our approach achieves state-of-the-art performance and shows great generalization ability.