BIQA (Blind Image Quality Assessment) is an important field of study that evaluates images automatically. Although significant progress has been made, blind image quality assessment remains a difficult task since images vary in content and distortions. Most algorithms generate quality without emphasizing the important region of interest. In order to solve this, a multi-stream spatial and channel attention-based algorithm is being proposed. This algorithm generates more accurate predictions with a high correlation to human perceptual assessment by combining hybrid features from two different backbones, followed by spatial and channel attention to provide high weights to the region of interest. Four legacy image quality assessment datasets are used to validate the effectiveness of our proposed approach. Authentic and synthetic distortion image databases are used to demonstrate the effectiveness of the proposed method, and we show that it has excellent generalization properties with a particular focus on the perceptual foreground information.

翻译：盲图像质量评估（BIQA）是自动评估图像的重要研究领域。尽管已取得显著进展，但由于图像内容和失真的多样性，盲图像质量评估仍是一项艰巨任务。多数算法在生成质量分数时未能强调关键感兴趣区域。为解决这一问题，本文提出一种基于多流空间与通道注意力的算法。该算法通过融合两个不同骨干网络的混合特征，并在此基础上引入空间与通道注意力机制对感兴趣区域赋予高权重，从而生成与人类感知评估高度相关的更精准预测。我们采用四个经典图像质量评估数据集验证所提方法的有效性。通过真实失真与合成失真图像数据库的测试，结果表明该方法具有优异的泛化性能，尤其关注感知前背景信息。