Journal of Multimedia Processing and Technologies Hsing-Cheng Liu, Yao-Liang Chung 17 4 2026 https://doi.org/10.6025/jmpt/2026/17/4/185-207 https://www.dline.info/jmpt/fulltext/v17n4/jmptv17n4_1.pdf Underwater image degradation severely limits the effectiveness of computer vision systems deployed on autonomous underwater vehicles (AUVs) and remotely operated vehicles (ROVs). While underwater image enhancement (UIE) is commonly used as a preprocessing step, improving perceptual visual quality does not necessarily guarantee improved performance in downstream tasks, nor does a single restoration strategy suffice for heterogeneous degradation types. This study presents a controlled, scene independent framework to systematically evaluate underwater degradation, restoration, and representation learning. Using the Synthetic Underwater Image Dataset (SUID) comprising 30 ground truth scenes and 900 synthetically degraded variants across four categories (Bluish, Greenish, Hazy, and Low Light), we conducted experiments in degradation classification, paired affine restoration, multi-task learning, and contrastive representation analysis. To prevent data leakage, all evaluations utilized a strict scene independent partitioning strategy. Results indicate that while chromatic and illumination degradations (Bluish, Greenish, Low Light) are effectively mitigated by condition specific affine transformations (yielding up to +14.5 dB PSNR gain), physical scattering (Hazy) resists simple affine correction. A shared multi task representation successfully balanced degradation classification (Macro-F1: 0.780), quality prediction, and restoration (PSNR: 20.91 dB). Furthermore, contrastive learning achieved a 94.4% same scene retrieval rate, demonstrating that embeddings can preserve underlying scene identity while retaining degradation sensitivity. Ultimately, this framework establishes a rigorous benchmark for degradation specific behavior and provides a reproducible protocol for future synthetic to real domain adaptation in underwater vision.