AI Article Synopsis
- * The algorithm effectively prioritizes important image regions by integrating feature maps from two different backbones, leading to improved detail capture.
- * Evaluation results show that this method outperforms existing techniques and aligns closely with human perception, demonstrating strong generalization on various databases and a focus on perceptual foreground information.
Article Abstract
This study introduces a novel Blind Image Quality Assessment (BIQA) approach leveraging a multi-stream spatial and channel attention model. Our method addresses challenges posed by diverse image content and distortions by integrating feature maps from two distinct backbones. Through spatial and channel attention mechanisms, our algorithm prioritizes regions of interest, enhancing its ability to capture crucial image details. Extensive evaluations on four benchmark datasets demonstrate superior performance compared to existing methods, closely aligning with human perceptual assessment. Our approach exhibits exceptional generalization capabilities on both authentic and synthetic distortion databases. Moreover, it demonstrates a distinctive focus on perceptual foreground information, enhancing its practical applicability. Thorough quantitative analyses underscore the algorithm's superior performance, establishing its dominance over existing methods.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11522308 | PMC |
| http://dx.doi.org/10.1038/s41598-024-77076-4 | DOI Listing |
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