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

TL;DR

This paper introduces a novel approach combining convolutional autoencoders with DNA-adapted entropy coding to efficiently compress and store images in synthetic DNA, addressing error-prone DNA storage processes.

Contribution

It presents a new DNA storage-compatible image compression method using autoencoders and specialized entropy coders, improving over previous fixed-length coding techniques.

Findings

Enhanced compression efficiency for DNA storage

Improved error resilience in DNA data encoding

Effective latent space quantization for DNA storage

Abstract

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.