  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.
