Towards learning-based image compression for storage on DNA support

Sophie Strebel; Noémie Monnier; Davi Lazzarotto; Michela Testolina; Touradj Ebrahimi

doi:10.1117/12.2677356

4 October 2023 Towards learning-based image compression for storage on DNA support

Sophie Strebel, Noémie Monnier, Davi Lazzarotto, Michela Testolina, Touradj Ebrahimi

Proceedings Volume 12674, Applications of Digital Image Processing XLVI; 126740V (2023) https://doi.org/10.1117/12.2677356
Event: SPIE Optical Engineering + Applications, 2023, San Diego, California, United States

Abstract

The demand for data storage has been growing exponentially over the past decades. Current techniques have significant shortcomings, such as high resource requirements and a lack of sufficient longevity. In contrast, research on DNA-based storage has been advancing notably due to its low environmental impact, larger capacity, and longer lifespan. This led to the development of compression methods that adapted the binary representation of legacy JPEG images into a quaternary base of nucleotides following the biochemical constraints of current synthesis and sequencing mechanisms. In this work, we show that DNA can also be leveraged to efficiently store images compressed with neural networks even without retraining, by combining a convolutional autoencoder with a Goldman encoder. The proposed method is compared to the state of the art, resulting in higher compression efficiency on two different datasets when evaluated by a number of objective quality metrics.

Conference Presentation

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Sophie Strebel, Noémie Monnier, Davi Lazzarotto, Michela Testolina, and Touradj Ebrahimi "Towards learning-based image compression for storage on DNA support", Proc. SPIE 12674, Applications of Digital Image Processing XLVI, 126740V (4 October 2023); https://doi.org/10.1117/12.2677356

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