Utility-Centered Underwater Image Quality Evaluation

Abstract

In recent decades, the emergence of image applications has greatly facilitated the development of vision-based tasks. As a result, image quality assessment (IQA) has become increasingly significant for monitoring, controlling, and improving visual signal quality. While existing IQA methods focus on image fidelity and aesthetics to characterize perceived quality, it is important to evaluate the utility-centered quality of an image for popular tasks, such as object detection. However, research shows that there is a low correlation between utilities and perceptions. To address this issue, this article proposes a utility-centered IQA approach. Specifically, our research focuses on underwater fish detection as a challenging task in an underwater environment. Based on this task, we have developed a utility-centered underwater image quality database (UIQD) and a transfer learning-based advanced underwater quality by utility assessment (AQUA). Inspired by the top–down design approach used in fidelity-oriented IQA methods, we utilize deep models of object detection and transfer their features to the mission of utility-centered quality evaluation. Experimental results validate that the proposed AQUA achieves promising performance not only in fish detection but also in other tasks such as face recognition. We believe that our research provides valuable insights to bridge the gap between IQA research and visual tasks.