MS-SCANet: A Multiscale Transformer-Based Architecture with Dual Attention for No-Reference Image Quality Assessment

• URL: http://arxiv.org/abs/2602.04032v1
• Date: Tue, 03 Feb 2026 21:43:15 GMT
• Title: MS-SCANet: A Multiscale Transformer-Based Architecture with Dual Attention for No-Reference Image Quality Assessment
• Authors: Mayesha Maliha R. Mithila, Mylene C. Q. Farias,
• Abstract summary: Multi-Scale Spatial Channel Attention Network (MS-SCANet) is a transformer-based architecture designed for no-reference image quality assessment (IQA)<n>MS-SCANet features a dual-branch structure that processes images at multiple scales, effectively capturing both fine and coarse details.
• Score: 1.7188280334580195
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present the Multi-Scale Spatial Channel Attention Network (MS-SCANet), a transformer-based architecture designed for no-reference image quality assessment (IQA). MS-SCANet features a dual-branch structure that processes images at multiple scales, effectively capturing both fine and coarse details, an improvement over traditional single-scale methods. By integrating tailored spatial and channel attention mechanisms, our model emphasizes essential features while minimizing computational complexity. A key component of MS-SCANet is its cross-branch attention mechanism, which enhances the integration of features across different scales, addressing limitations in previous approaches. We also introduce two new consistency loss functions, Cross-Branch Consistency Loss and Adaptive Pooling Consistency Loss, which maintain spatial integrity during feature scaling, outperforming conventional linear and bilinear techniques. Extensive evaluations on datasets like KonIQ-10k, LIVE, LIVE Challenge, and CSIQ show that MS-SCANet consistently surpasses state-of-the-art methods, offering a robust framework with stronger correlations with subjective human scores.

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