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

Blind Image Quality Assessment Using Multi-Stream Architecture with Spatial and Channel Attention