Summary of Exploring the Limits Of Semantic Image Compression at Micro-bits Per Pixel, by Jordan Dotzel et al.
Exploring the Limits of Semantic Image Compression at Micro-bits per Pixel
by Jordan Dotzel, Bahaa Kotb, James Dotzel, Mohamed Abdelfattah, Zhiru Zhang
First submitted to arxiv on: 21 Feb 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 research paper explores the frontier of image compression, comparing traditional methods with semantic compression techniques that represent concepts and relationships using natural language. The study uses GPT-4V and DALL-E3 from OpenAI to investigate the quality-compression tradeoff for images. The authors find that semantic compression can operate at extremely low bitrates, disregarding structural information like location, size, and orientation, pushing the limits of current technology as low as 100 μbpp (up to 10,000 times smaller than JPEG). The paper hypothesizes that this 100 μbpp level represents a soft limit on semantic compression for standard image resolutions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re trying to compress an image. Traditional methods work well by looking at the tiny details in the picture, like pixel values or frequency content. But what if you could store just the important concepts and relationships in the image? That’s basically what text-based semantic compression does! It can make images much, much smaller – up to 10,000 times smaller than usual! The researchers used powerful AI models from OpenAI to see how well this method works. They found that it can even work at very low levels of detail, like 100 μbpp. This is a big deal because it could help us compress images in new and exciting ways. |
Keywords
» Artificial intelligence » Gpt
