HybridFlow-DNA: A Deep Generative Compression Framework for DNA Storage of Images

DNA storage has emerged as a promising solution to address the exponentially growing demand for storage capacity, offering advantages in density, stability, and long-term preservation potential. Currently, image compression for DNA storage has evolved into learned image compression (LIC), particularly through the application of deep learning methods based on artificial neural networks. The present study proposes a novel image compression framework for DNA Storage, named HybridFlow-DNA. HybridFlow-DNA is established by integration of VQGAN and MLIC with the adaptive dynamic DNA fountain encoding scheme. Experimental results demonstrate that HybridFlow-DNA achieves a high virtual information capacity while effectively maintaining the fidelity of the reconstruction of images.