AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
A two-stage framework integrating degradation-aware self-supervised contrastive pre-training with frequency-spatial domain feature learning is proposed, offering a reliable solution for NR-IQA in real-world multimedia systems and advancing the alignment between computational quality assessment and human visual perception.
A novel Hierarchical Multi-Scale Cross-Attention Network that effectively captures both local distortion patterns and global semantic information for quality prediction and exhibits superior generalization capability compared to existing approaches is proposed.
Three targeted enhancements to No-Reference Image Quality Assessment show state-of-the-art results on all three synthetic benchmarks and reveal configuration-dependent patterns on authentic-distortion data.
A Swin Transformer-based NR-IQA method comprising three core modules, which improves Spearman Rank-Order Correlation Coefficient and Pearson Linear Correlation Coefficient over the best-performing comparison method, validating its effectiveness for no-reference image quality prediction.
No-reference image quality assessment (NR-IQA) quantifies image distortion. It plays an important role in computer vision. Distorted images vary greatly in content. Many existing methods tend to fuse content information with quality prediction. However, they often overlook human visual perception. To address this issue...
No-Reference Image Quality Assessment (NR-IQA) aims to predict perceptual image quality from distorted images without reference signals. Existing NR-IQA methods often incorporate visual attention through external weighting or feature fusion, while semantic and distortion cues are often not sufficiently organized for qu...
The proposed MSGF-Net embeds a Hybrid Gradient-Frequency Attention Module (HG-FAM) within the residual bottlenecks to achieve dual-domain synergistic perception of global spectral energy and spatial gradient details and significantly outperforms existing main-stream methodologies.
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