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.

提出了一种基于多流空间和通道注意力的算法，通过结合来自两个不同backbones的混合特征，并进行空间和通道的注意力，以提供对感兴趣区域的高权重，生成更准确的预测结果。使用四个传统图像质量评估数据集验证了我们提出的方法的有效性，使用真实和合成的畸变图像数据库展示了该方法的有效性，并显示出它在感知为前景信息方面具有出色的泛化性能。

多流架构中的盲图像质量评估利用空间和通道注意力