Underwater Image Quality Evaluation: A Comprehensive Review
Mengjiao Shen, Miao Yang, Jinyang Zhong, Hantao Liu, Can Pan · IET Image Processing · 2025
ABSTRACT Underwater image quality evaluation (UIQE) is crucial in improving image processing techniques and optimizing the design of the imaging system to obtain object information more accurately. However, existing UIQE methods are designed based on limited images or consider only a few natural scene statistics (NSS) metrics, lacking consideration for generalization across various underwater imaging applications. In this paper, an in‐depth review of the existing UIQE methods based on evaluation operations is provided, emphasizing the bias present when evaluating UIQE methods using individual metrics. To address this, a novel metric called quadrilateral datum evaluation (QDE) is designed for UIQE methods. It comprehensively considers robustness across different datasets, as well as correlation and ranking consistency with mean opinion scores (MOS). This is the first solution to measure an UIQE method from an all‐encompassing visual perspective. By using QDE, UIQE methods characterized by greater feature strength and small imbalance demonstrate good consistency and robustness across multiple aspects, providing a basis for the design of UIQE methods.