Region-Aware Degradation-Conditioned Vision-Language Learning for Blind Image-Quality Assessment of Power Field-Operation Imagery

Reliable blind image-quality assessment is essential for visual monitoring of power-field operations, where images captured by fixed cameras, unmanned aerial vehicles, and handheld terminals are frequently affected by blur, noise, uneven exposure, compression, and resolution loss. These degradations are often spatially non-uniform, and distortions occurring in task-relevant regions usually have a greater impact on operation-safety assessment than those appearing in background regions. Existing methods, however, commonly rely on global representations or treat local regions uniformly, making it difficult to identify degradations in task-critical areas and generalize to complex industrial imagery. To address this problem, we propose RDC-IQA, a region-aware degradation-conditioned vision-language framework. It combines hierarchical visual tokens with contrastively learned degradation priors through confidence-aware residual conditioning, preserving semantic representations while improving sensitivity to local distortions. The resulting regional features are aligned with structured descriptions of scene content, distortion attributes, and quality levels. Regional contributions are then determined jointly by task relevance and degradation severity, so that impaired task-relevant regions receive greater attention during quality prediction. In addition, a local degradation ranking objective learns spatial quality variations without requiring patch-level subjective annotations. Experiments on six public benchmarks and the PowerIQA dataset demonstrate that RDC-IQA achieves strong performance across synthetic, authentic, and power field-operation imagery.