Summary of End-to-end Optimized Image Compression with the Frequency-oriented Transform, by Yuefeng Zhang and Kai Lin
End-to-End Optimized Image Compression with the Frequency-Oriented Transform
by Yuefeng Zhang, Kai Lin
First submitted to arxiv on: 16 Jan 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Multimedia (cs.MM)
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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 proposes an end-to-end optimized image compression model that leverages frequency-oriented transforms to enable scalable coding and superior performance. Building upon previous work in deep learning-based image compression, the authors address the challenge of interpretability by introducing a novel transform that separates the original image signal into distinct frequency bands, aligning with human-interpretable concepts. The proposed model consists of four components: spatial sampling, frequency-oriented transform, entropy estimation, and frequency-aware fusion. Experimental results demonstrate that the model outperforms traditional codecs, including next-generation standard H.266/VVC, on the MS-SSIM metric, while also preserving semantic fidelity in visual analysis tasks such as object detection and semantic segmentation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper has developed a new way to compress images using deep learning methods. The goal is to make image compression more efficient and better than existing ways of doing it. The researchers created a special kind of transform that separates an image into different parts, based on the frequency (or pattern) of the image. This allows for more efficient compression and helps preserve important details in the image. They tested their method and found that it worked better than other methods, including a new standard called H.266/VVC. |
Keywords
» Artificial intelligence » Deep learning » Object detection » Semantic segmentation
