Summary of Unifying Generation and Compression: Ultra-low Bitrate Image Coding Via Multi-stage Transformer, by Naifu Xue et al.
Unifying Generation and Compression: Ultra-low bitrate Image Coding Via Multi-stage Transformer
by Naifu Xue, Qi Mao, Zijian Wang, Yuan Zhang, Siwei Ma
First submitted to arxiv on: 6 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG); 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 Unified Image Generation-Compression (UIGC) paradigm combines image generation and compression processes using vector-quantized (VQ) image models and a multi-stage transformer to model the prior distribution of image content. This framework utilizes learned priors for entropy estimation, aiding in the regeneration of lost tokens. The authors demonstrate the effectiveness of UIGC over existing codecs in ultra-low bitrate scenarios (<=0.03 bpp), showcasing its potential in generative compression. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Generative compression technology has improved the quality of compressed data, but often neglects capturing the prior distribution of image content, hindering further bitrate reduction. This paper introduces a new way to compress images by combining generation and compression processes. It uses special models to capture patterns in images and helps regenerate lost information. The results show that this method is better than existing codes at very low bitrates (<=0.03 bpp). |
Keywords
* Artificial intelligence * Image generation * Transformer
