Summary of Efficient Neural Network Encoding For 3d Color Lookup Tables, by Vahid Zehtab et al.
Efficient Neural Network Encoding for 3D Color Lookup Tables
by Vahid Zehtab, David B. Lindell, Marcus A. Brubaker, Michael S. Brown
First submitted to arxiv on: 19 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); 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 This paper presents a neural network architecture that can efficiently encode and reconstruct hundreds of 3D color lookup tables (LUTs) with minimal memory footprint. The proposed model, which is designed for applications such as video editing, in-camera processing, and display color processing, achieves a memory footprint of less than 0.25 MB while maintaining accurate color reconstruction across the entire color gamut. Additionally, the network can be modified to provide further quality gains on natural image colors. Furthermore, the authors demonstrate that the model can be used for bijective encoding, allowing for reverse color processing. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way for computers to store and use lots of 3D color maps (called lookup tables) in a very small amount of memory space. This is important because many devices and software programs need to have hundreds or even thousands of these color maps on hand, but they can’t fit all of them into their memory at once. The new system uses special computer models called neural networks to store the color maps in a way that takes up much less space while still being able to recall the right colors quickly and accurately. |
Keywords
* Artificial intelligence * Neural network * Recall
