Blind Image Quality Assessment Using Multi-Stream Architecture with Spatial and Channel Attention
원문 초록
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.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Taylor expansion-based Kolmogorov-Arnold network for blind image quality assessment
Attention Down-Sampling Transformer, Relative Ranking and Self-Consistency for Blind Image Quality Assessment
No-Reference Image Quality Assessment with Global-Local Progressive Integration and Semantic-Aligned Quality Transfer
DDR: Exploiting Deep Degradation Response as Flexible Image Descriptor
Bridging the Synthetic-to-Authentic Gap: Distortion-Guided Unsupervised Domain Adaptation for Blind Image Quality Assessment
Computational Analysis of Degradation Modeling in Blind Panoramic Image Quality Assessment
토론
이 논문과 연결된 포럼 글이 아직 없습니다.