# Reversible Colour Density Compression of Images using cGANs

arXiv: https://arxiv.org/abs/2106.10542 · HTML: https://mlsanity.com/paper/2106.10542/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 11 references that are in the map.
- First posted on arXiv 19 Jun 2021.

## Builds on (most cited first, 7 of 7)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Improved Techniques for Training GANs](https://mlsanity.com/paper/1606.03498/) (2016)
- [Conditional Generative Adversarial Nets](https://mlsanity.com/paper/1411.1784/) (2014)
- [Generative Adversarial Networks for Extreme Learned Image Compression](https://mlsanity.com/paper/1804.02958/) (2018)
- [Jpeg Image Compression Using Discrete Cosine Transform - A Survey](https://mlsanity.com/paper/1405.6147/) (2014)
- [Near-lossless ℓ_∞-constrained Image Decompression via Deep Neural Network](https://mlsanity.com/paper/1801.07987/) (2018)

## Abstract (arXiv)

Image compression using colour densities is historically impractical to decompress losslessly. We examine the use of conditional generative adversarial networks in making this transformation more feasible, through learning a mapping between the images and a loss function to train on. We show that this method is effective at producing visually lossless generations, indicating that efficient colour compression is viable.