A constrained Shannon-Fano entropy coder for image storage in synthetic DNA

TL;DR

This paper introduces a modified Shannon-Fano coding algorithm tailored for DNA data storage, improving compression efficiency for long-term 'cold' data while respecting biochemical constraints, integrated into JPEG for image storage.

Contribution

It presents a novel constrained Shannon-Fano coding method specifically designed for DNA storage, enhancing compression ratios without quality loss.

Findings

Achieved 0.5 to 2 bits per nucleotide improvement in compression ratio.

Maintained image reconstruction quality with the new coding scheme.

Demonstrated suitability for long-term 'cold' data storage in DNA.

Abstract

The exponentially increasing demand for data storage has been facing more and more challenges during the past years. The energy costs that it represents are also increasing, and the availability of the storage hardware is not able to follow the storage demand's trend. The short lifespan of conventional storage media -- 10 to 20 years - forces the duplication of the hardware and worsens the situation. The majority of this storage demand concerns "cold" data, data very rarely accessed but that has to be kept for long periods of time. The coding abilities of synthetic DNA, and its long durability (several hundred years), make it a serious candidate as an alternative storage media for "cold" data. In this paper, we propose a variable-length coding algorithm adapted to DNA data storage with improved performance. The proposed algorithm is based on a modified Shannon-Fano code that respects some biochemichal constraints imposed by the synthesis chemistry. We have inserted this code in a JPEG compression algorithm adapted to DNA image storage and we highlighted an improvement of the compression ratio ranging from 0.5 up to 2 bits per nucleotide compared to the state-of-the-art solution, without affecting the reconstruction quality.