Summary of Sensitivity Decouple Learning For Image Compression Artifacts Reduction, by Li Ma et al.
Sensitivity Decouple Learning for Image Compression Artifacts Reduction
by Li Ma, Yifan Zhao, Peixi Peng, Yonghong Tian
First submitted to arxiv on: 15 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 This paper proposes a novel approach for reducing image compression artifacts by decoupling intrinsic attributes into two complementary features. Unlike previous methods that solely focus on learning a mapping from compressed images to originals, this method recognizes the importance of considering the compression degree and high-level semantic representations. The Dual Awareness Guidance Network (DAGN) employs adversarial training to regularize encoded features and develop a compression quality-aware feature encoder. A cross-feature fusion module is also introduced to maintain consistency in compression-insensitive features. Experimental results on various datasets demonstrate an average 2.06 dB PSNR gains, outperforming state-of-the-art methods, with a processing time of only 29.7 ms. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps reduce image compression artifacts by looking at images in two different ways. Most previous methods just tried to turn compressed images back into the original, but this method also considers how the compression affected the image. It uses a special kind of training called adversarial training to help the model learn about both aspects. The result is a new way of processing images that is faster and better than before. This could be useful for tasks like object detection and image recognition. |
Keywords
» Artificial intelligence » Encoder » Object detection
