Summary of A Two-fold Patch Selection Approach For Improved 360-degree Image Quality Assessment, by Abderrezzaq Sendjasni et al.
A Two-Fold Patch Selection Approach for Improved 360-Degree Image Quality Assessment
by Abderrezzaq Sendjasni, Seif-Eddine Benkabou, Mohamed-Chaker Larabi
First submitted to arxiv on: 17 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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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 The paper presents a novel approach to improving the accuracy of 360-degree perceptual image quality assessment (IQA) by combining visual patch selection with embedding similarity-based refinement. The method consists of two stages: the first stage selects patches from 360-degree images using three distinct sampling methods, while the second stage uses an embedding similarity-based process to filter and prioritize the most informative patches. This dual selection mechanism ensures that the training data is both relevant and informative, enhancing the model’s learning efficiency. Experimental results on three benchmark datasets demonstrate the effectiveness of the proposed method, achieving performance gains of up to 4.5% in accuracy and monotonicity of quality score prediction while using only 40-50% of the training patches. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper improves how computers assess image quality in 360-degree images. It’s like taking a big picture and breaking it down into smaller pieces to help the computer learn what makes good or bad image quality. The new approach is better than before, with results showing that it can get things right 4.5% more often while using less information. |
Keywords
» Artificial intelligence » Embedding
