An Underwater Image Quality Dataset with Renewed Pairwise Voting
Mengjiao Shen, Miao Yang, Hansen Zhang, Jinyang Zhong, Yuquan Qiu, Jinwei Gu
Abstract
Image datasets with paired mean opinion scores (MOS) enable the quantization of the perceptual differences between images and are of great significance for images taken underwater. In our previous research, a pairwise label underwater image quality subjective ranking (PLUIQR) method was proposed. In this paper, we take a further step by designing a post-reliability verification for the PLUIQR and releasing a publicly accessible underwater image quality dataset called PCUID. For the raw paired voting, steps including a triangular cycle error (TCE) criterion and reprocessing of the dispute data are performed. Meanwhile, the group maximum differentiation (gMAD) method is used to evaluate the performance of underwater quality assessment methods (UIQA) on images with similar quality. That illustrates the proposed dataset enabling UIQA methods to their judgment toward subtle quality differences. The dataset is available at https://github.com/JOU-UIP/PCUID.
BibTeX
@inproceedings{icassp2025_anunderwaterimag,
title = {An Underwater Image Quality Dataset with Renewed Pairwise Voting},
author = {Mengjiao Shen and Miao Yang and Hansen Zhang and Jinyang Zhong and Yuquan Qiu and Jinwei Gu},
booktitle = {ICASSP 2025},
year = {2025}
}