Summary of Cross-iqa: Unsupervised Learning For Image Quality Assessment, by Zhen Zhang
Cross-IQA: Unsupervised Learning for Image Quality Assessment
by Zhen Zhang
First submitted to arxiv on: 7 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Image and Video Processing (eess.IV)
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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 proposed Cross-IQA method, based on a vision transformer (ViT) model, enables automatic perception of image quality without referencing original images. This no-reference image quality assessment (NR-IQA) technique can learn image quality features from unlabeled data. By constructing a pretext task for synthesized image reconstruction using ViT blocks, the proposed Cross-IQA method unsupervisedly extracts image quality information. The pre-trained encoder is then fine-tuned for linear regression score prediction. Experimental results demonstrate state-of-the-art performance in assessing low-frequency degradation information (e.g., color change, blurring) compared to classical full-reference IQA and NR-IQA under the same datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new way to assess image quality without needing the original picture. This is useful because many people share images online, but they might not have the high-quality version. The method uses a special kind of AI model called ViT to learn what makes an image good or bad. It works by practicing on fake images and then using that knowledge to predict how good a real image is. Tests showed that this method performs better than others at detecting problems like blurry pictures or changed colors. |
Keywords
» Artificial intelligence » Encoder » Linear regression » Vision transformer » Vit
