# Image storage on synthetic DNA using compressive autoencoders and DNA-adapted entropy coders

arXiv: https://arxiv.org/abs/2306.12882 · HTML: https://mlsanity.com/paper/2306.12882/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 22 references that are in the map.
- First posted on arXiv 22 Jun 2023.

## Builds on (most cited first, 10 of 10)

- [Variational image compression with a scale hyperprior](https://mlsanity.com/paper/1802.01436/) (2018)
- [Lossy Image Compression with Compressive Autoencoders](https://mlsanity.com/paper/1703.00395/) (2017)
- [Full Resolution Image Compression with Recurrent Neural Networks](https://mlsanity.com/paper/1608.05148/) (2016)
- [Variable Rate Deep Image Compression With a Conditional Autoencoder](https://mlsanity.com/paper/1909.04802/) (2019)
- [Variable Rate Deep Image Compression with Modulated Autoencoder](https://mlsanity.com/paper/1912.05526/) (2019)
- [Autoencoder based image compression: can the learning be quantization independent?](https://mlsanity.com/paper/1802.09371/) (2018)
- [A biologically constrained encoding solution for long-term storage of images onto synthetic DNA](https://mlsanity.com/paper/1904.03024/) (2019)
- [A constrained Shannon-Fano entropy coder for image storage in synthetic DNA](https://mlsanity.com/paper/2203.09988/) (2022)
- [Image processing in DNA](https://mlsanity.com/paper/1910.10095/) (2019)
- [Rotating labeling of entropy coders for synthetic DNA data storage](https://mlsanity.com/paper/2304.00493/) (2023)

## Abstract (arXiv)

Over the past years, the ever-growing trend on data storage demand, more specifically for "cold" data (rarely accessed data), has motivated research for alternative systems of data storage. Because of its biochemical characteristics, synthetic DNA molecules are now considered as serious candidates for this new kind of storage. This paper presents some results on lossy image compression methods based on convolutional autoencoders adapted to DNA data storage, with synthetic DNA-adapted entropic and fixed-length codes. The model architectures presented here have been designed to efficiently compress images, encode them into a quaternary code, and finally store them into synthetic DNA molecules. This work also aims at making the compression models better fit the problematics that we encounter when storing data into DNA, namely the fact that the DNA writing, storing and reading methods are error prone processes. The main take aways of this kind of compressive autoencoder are our latent space quantization and the different DNA adapted entropy coders used to encode the quantized latent space, which are an improvement over the fixed length DNA adapted coders that were previously used.