Enhancing Underwater Image Quality Assessment with Influential Perceptual Features

Abstract

In the multifaceted field of oceanic engineering, the quality of underwater images is paramount for a range of applications, from marine biology to robotic exploration. This paper presents a novel approach in underwater image quality assessment (UIQA) that addresses the current limitations by effectively combining low-level image properties with high-level semantic features. Traditional UIQA methods predominantly focus on either low-level attributes such as brightness and contrast or high-level semantic content, but rarely both, which leads to a gap in achieving a comprehensive assessment of image quality. Our proposed methodology bridges this gap by integrating these two critical aspects of underwater imaging. We employ the least-angle regression technique for balanced feature selection, particularly in high-level semantics, to ensure that the extensive feature dimensions of high-level content do not overshadow the fundamental low-level properties. The experimental results of our method demonstrate a remarkable improvement over existing UIQA techniques, establishing a new benchmark in both accuracy and reliability for underwater image assessment.