A Lightweight and Unified Framework for Underwater Image Utility Evaluation

Transmitting images through narrow-bandwidth underwater acoustic channels presents a significant challenge. However, for many applications, transmitting the entire image is not necessary. Instead, conveying only the critical information is typically sufficient to ensure task success. To address this, we have introduced a preprocessing strategy at the image acquisition stage, which involves down-sampling or feature extraction to distill the data into its essential components. This strategy is guided by utility considerations to ensure that the transmitted data is both concise and informative. However, existing quality evaluation methods are inadequate for providing accurate utility assessments and fail to simultaneously evaluate the utility of both images and features within a unified framework. To bridge this gap in underwater object detection (one of the typical underwater task), we have developed an underwater image utility dataset and introduced the Lightweight and Unified Utility for Underwater Image Evaluation (LU3IE). LU3IE leverages features derived from multiple detection models to enhance generalization. It then refines these features through dimensionality reduction and commonality extraction, and simplifies complexity through the use of knowledge distillation. Experimental results validate the effectiveness and scalability of LU3IE in accurately assessing the utility of underwater images.