论文标题
使用自动编码器上的合成DNA图像存储
Image Storage on Synthetic DNA Using Autoencoders
论文作者
论文摘要
在过去的几年中，数据存储需求的不断增长的趋势，更具体地用于“冷”数据（很少访问的数据），激发了替代数据存储系统的研究。由于其生化特征，合成DNA分子现在被认为是这种新型存储的严重候选者。本文根据适合DNA数据存储的卷积自动编码器提供了一些有损图像压缩方法的结果。 此处介绍的模型体系结构旨在有效地压缩图像，将其编码为第四纪代码，并最终将其存储到合成的DNA分子中。这项工作还旨在使压缩模型更好地符合我们将数据存储到DNA中时遇到的问题，即DNA编写，存储和阅读方法是错误的过程。由于我们在训练过程中使用的噪声模型，这种压缩自动编码器的主要取消是我们的量化和替换错误的稳健性。
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. 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 away of this kind of compressive autoencoder is our quantization and the robustness to substitution errors thanks to the noise model that we use during training.
