  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.
