ECAIQA: Efficient Cross-Dimensional Attention Network for No-Reference Image Quality Assessment

Existing no-reference image quality assessment (NR-IQA) methods struggle with distortions from generative adversarial networks. We propose ECAIQA, an Efficient Cross-dimensional Attention Network integrating Vision Transformer (ViT) with two novel modules: Convolutional Triplet Attention Module (CTAM) for channel-spatial interaction and Scale Swin Transformer Block (SSTB) for multi-scale global-local feature fusion. A dual-branch architecture aggregates patch-weighted quality scores, achieving state-of-the-art performance across eight benchmarks (LIVE, TID2013, CSIQ, KADID-10K, PIPAL).