DNA exhibits remarkable potential as a data storage solution due to its impressive storage density and long-term stability, stemming from its inherent biomolecular structure. However, developing this novel medium comes with its own set of challenges, particularly in addressing errors arising from storage and biological manipulations. These challenges are further conditioned by the structural constraints of DNA sequences and cost considerations. In response to these limitations, we have pioneered a novel compression scheme and a cutting-edge Multiple Description Coding (MDC) technique utilizing neural networks for DNA data storage. Our MDC method introduces an innovative approach to encoding data into DNA, specifically designed to withstand errors effectively. Notably, our new compression scheme overperforms classic image compression methods for DNA-data storage. Furthermore, our approach exhibits superiority over conventional MDC methods reliant on auto-encoders. Its distinctive strengths lie in its ability to bypass the need for extensive model training and its enhanced adaptability for fine-tuning redundancy levels. Experimental results demonstrate that our solution competes favorably with the latest DNA data storage methods in the field, offering superior compression rates and robust noise resilience.

翻译：脱氧核糖核酸（DNA）凭借其固有的生物分子结构，展现出作为数据存储解决方案的巨大潜力，具有卓越的存储密度和长期稳定性。然而，这一新型介质的开发面临诸多挑战，特别是存储与生物操作过程中产生的错误，且这些挑战进一步受限于DNA序列的结构约束与成本考量。针对上述限制，我们率先提出了一种新型压缩方案与基于神经网络的尖端多描述编码（MDC）技术，用于DNA数据存储。我们的MDC方法引入了一种创新的数据编码方式，专为有效抵御错误而设计。值得注意的是，我们的新型压缩方案在DNA数据存储性能上超越了传统图像压缩方法。此外，本方法相较于依赖自编码器的传统MDC技术展现出优越性，其独特优势在于无需进行大规模模型训练，且能灵活调节冗余度。实验结果表明，我们的解决方案在压缩率与抗噪声鲁棒性方面与领域内最新DNA数据存储方法相比具有竞争力。