Image Quality Caption with Attentive and Recurrent Semantic Attractor Network

Wen Yang, Jinjian Wu, Leida Li, Weisheng Dong, Guangming Shi

Abstract: In this paper, a novel quality caption model is inventively developed to assess the image quality with hierarchical semantics. Existing image quality assessment (IQA) methods usually represent image quality with a quantitative value, resulting in inconsistency with human cognition. Generally, human beings are good at perceiving image quality in terms of semantic description rather than quantitative value. Moreover, cognition is a needs-oriented task where hierarchical semantics are extracted. The mediocre quality value fails to reflect degradations on hierarchical semantics. Therefore, a new IQA framework is proposed to describe the quality for needs-oriented cognition. A novel quality caption procedure is firstly introduced, in which the quality is represented as patterns of activation distributed across the diverse degradations on hierarchical semantics. Then, an attentive and recurrent semantic attractor network (ARSANet) is designed to activate the distributed patterns for image quality description. Experiments demonstrate that our method achieves superior performance and is highly compliant with human cognition.