MS-SCANet：一种基于多尺度变换器的双注意力架构用于无参考图像质量评估
MS-SCANet: A Multiscale Transformer-Based Architecture with Dual Attention for No-Reference Image Quality Assessment
AI总结：
MS-SCANet通过多尺度变换器架构和双注意力机制，实现无参考图像质量评估的高精度与高效性。
摘要：
我们提出了多尺度空间通道注意力网络（MS-SCANet），一种用于无参考图像质量评估（IQA）的基于变换器的架构。MS-SCANet具有双分支结构，能够在多个尺度上处理图像，有效捕捉细粒度和粗粒度细节，优于传统单尺度方法。通过集成定制化的空间和通道注意力机制，我们的模型强调关键特征，同时减少计算复杂性。MS-SCANet的关键组件是其跨分支注意力机制，该机制增强了不同尺度之间特征的整合，解决了先前方法的局限性。我们还引入了两种新的一致性损失函数，即跨分支一致性损失和自适应池化一致性损失，这些损失函数在特征缩放过程中保持空间完整性，优于传统线性和双线性技术。在KonIQ-10k、LIVE、LIVE挑战和CSIQ等数据集上的广泛评估表明，MS-SCANet在多个方面都优于最先进的方法，提供了一个稳健的框架，与主观人类评分有更强的相关性。
英文摘要：
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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