Blind Image Quality Assessment Using Multi-Stream Architecture with Spatial and Channel Attention
This work addresses the challenge of automatically evaluating image quality for applications like photography and media, but it is incremental as it builds on existing attention mechanisms.
The paper tackles the problem of blind image quality assessment (BIQA) by proposing a multi-stream architecture with spatial and channel attention to emphasize important regions, resulting in more accurate predictions with high correlation to human perceptual assessment, validated on four legacy datasets.
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.