Summary of Extreme Compression Of Adaptive Neural Images, by Leo Hoshikawa et al.
Extreme Compression of Adaptive Neural Images
by Leo Hoshikawa, Marcos V. Conde, Takeshi Ohashi, Atsushi Irie
First submitted to arxiv on: 27 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Graphics (cs.GR); 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 introduces Implicit Neural Representations (INRs) and Neural Fields as a novel paradigm for signal representation, which can be applied to various types of data such as images, audio, 3D scenes, and videos. The approach represents a signal as a continuous and differentiable neural network, offering benefits like continuous resolution and memory efficiency. However, this method poses new challenges, including compressing the neural image without losing sensitive details or fidelity. To address this challenge, the authors propose Adaptive Neural Images (ANI), an efficient neural representation that adapts to different inference or transmission requirements. The proposed method reduces the bits-per-pixel (bpp) of the neural image by 4x while maintaining its quality. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to represent images using special types of artificial intelligence networks called neural fields. Instead of breaking down an image into tiny pieces like most computer programs do, these neural fields treat the entire image as one big puzzle that can be solved in many different ways. This approach has some really cool benefits, like being able to see the same image in super high resolution or having a much smaller file size without losing any important details. |
Keywords
* Artificial intelligence * Inference * Neural network
