BWAFDA: Block-wise Weighted Attention Fusion with Detail-aware for No-Reference Image Quality Assessment

We present BWAFDA, a dual-branch no-reference image quality assessment model that replaces uniform averaging with detail-aware, block-wise fusion. The context encoder branch produces multi-scale block tokens, and a lightweight detail branch extracts high-frequency cues. These streams are combined late. First, block tokens are aggregated via block-wise attention across scales, guided by a soft multi-scale prior that stabilizes cross-scale interactions and preserves localized artifacts. The resulting descriptors are then fused per scale using learned weights. Subsequently, the detail cues are injected to refine distortion-sensitive evidence. Finally, a class-token-guided head performs “where-to-trust-what” selection over blocks to produce the quality score, without the need for region labels. Comprehensive evaluations on seven public NR-IQA benchmarks show state-of-the-art (SOTA) performance. Across multiple controlled ablations, we observe consistent drops in correlation compared to the full model, indicating that region-aware masking, contextual features, and multi-scale aggregation contribute jointly rather than redundantly. Overall, detail-aware, block-wise fusion delivers SOTA accuracy while preserving data efficiency and transferability by aligning localized artifact cues with global context.