  The main obstacles for the practical deployment of DNA-based data storage
platforms are the prohibitively high cost of synthetic DNA and the large number
of errors introduced during synthesis. In particular, synthetic DNA products
contain both individual oligo (fragment) symbol errors as well as missing DNA
oligo errors, with rates that exceed those of modern storage systems by orders
of magnitude. These errors can be corrected either through the use of a large
number of redundant oligos or through cycles of writing, reading, and rewriting
of information that eliminate the errors. Both approaches add to the overall
storage cost and are hence undesirable. Here we propose the first method for
storing quantized images in DNA that uses signal processing and machine
learning techniques to deal with error and cost issues without resorting to the
use of redundant oligos or rewriting. Our methods rely on decoupling the RGB
channels of images, performing specialized quantization and compression on the
individual color channels, and using new discoloration detection and image
inpainting techniques. We demonstrate the performance of our approach
experimentally on a collection of movie posters stored in DNA.
