Summary of Progressive Feature Fusion Network For Enhancing Image Quality Assessment, by Kaiqun Wu et al.
Progressive Feature Fusion Network for Enhancing Image Quality Assessment
by Kaiqun Wu, Xiaoling Jiang, Rui Yu, Yonggang Luo, Tian Jiang, Xi Wu, Peng Wei
First submitted to arxiv on: 13 Jan 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This research proposes a new framework for assessing the quality of images compressed using different algorithms. The existing methods struggle to distinguish subtle differences between distorted images generated by various approaches. To tackle this challenge, the authors develop a fine-grained network that extracts multi-scale features from images. A cross subtract block is designed to process positive and negative image pairs, enabling feature-based comparison. Additionally, a progressive feature fusion block fuses these features in a novel way, allowing hierarchical spatial 2D features to be processed gradually. Experimental results demonstrate the proposed framework’s superiority over current mainstream methods, ranking second in the CLIC benchmark. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research helps us better understand how to compare images that have been compressed using different techniques. Right now, it’s hard to tell which image is better just by looking at them because they all look a little distorted. To fix this problem, the authors create a new way to assess image quality. They use a special kind of network that looks at images in different ways and then combines these views to make a decision. The results show that their method works really well and can even beat other popular methods for assessing image quality. |
