Abstract
Underwater image quality assessment (UIQA) is a challenging task due to the complexities of underwater environments. Traditional UIQA methods primarily rely on fitting mean opinion scores (MOS), which are limited by human visual biases. To address the above limitation, we propose a no-reference underwater image quality assessment paradigm using reinforcement sequences. Our paradigm leverages reinforcement learning to iteratively merge the input image with the corresponding ground truth, generating an optimized sequence of images. A classifier generates probability arrays for the optimized sequence, which are converted into objective scores by a regression model. Unlike existing methods that focus solely on the final quality score, our paradigm emphasizes dynamic quality changes throughout the image-enhancement process. By employing objective mixing ratio labels, our reinforcement sequence dataset reduces subjective bias. The multiscale classifier captures local and global information differences between the input and ground truth images, effectively preserving the contrast and detail in diverse lighting conditions. Our paradigm combines multi-source data classification with support vector regression, optimizing the mapping of feature vectors to quality scores through fine-tuning libsvm kernel parameters. Experimental results on multiple benchmark datasets demonstrate that our paradigm outperforms the state-of-the-art UIQA methods, providing an effective solution for Underwater Image quality Assessment via Reinforcement Sequences (RSUIA). Our code will be available at: https://github.com/zhoujingchun03/RSUIA .