ABSTRACT

In the field of storing images into DNA, the code tables and universal error correction codes have the potential to mitigate the effect of base errors to a certain extent. However, they prove to be ineffective in dealing with indels (insertion and deletion errors), resulting in a decline in information density and the quality of reconstructed image. This paper proposes a novel encoding and decoding method named DP-ID for storing images into DNA that improves information density and the quality of reconstructed image. Firstly, the image is compressed as bitstreams by the dynamic programming algorithm. Secondly, the bitstreams obtained are mapped to DNA, which are then interleaved. The reconstructed image is obtained by applying median filtering to remove salt-and-pepper noise. Simulation results show the reconstructed image by DP-ID at 5% error rate is better than that by other methods at 1% error rate. This robustness to high errors is compatible with the unsatisfied biological constraints caused by high information density. Wet experiments show that DP-ID can reconstruct high quality image at 5X sequencing depth. The high information density and low sequencing depth significantly reduce the cost of DNA storage, facilitating the large-scale storage of images into DNA.