DRASIC: Distributed recurrent autoencoder for scalable image compression

Enmao Diao, Jie Ding, Vahid Tarokh

Research output: Chapter in Book/Report/Conference proceedingConference contribution


We propose a new architecture for distributed image compression from a group of distributed data sources. The work is motivated by practical needs of data-driven codec design, low power consumption, robustness, and data privacy. The proposed architecture, which we refer to as Distributed Recurrent Autoencoder for Scalable Image Compression (DRASIC), is able to train distributed encoders and one joint decoder on correlated data sources. Its compression capability is much better than the method of training codecs separately. Meanwhile, the performance of our distributed system with 10 distributed sources is only within 2 dB peak signal-to-noise ratio (PSNR) of the performance of a single codec trained with all data sources. We experiment distributed sources with different correlations and show how our data-driven methodology well matches the Slepian-Wolf Theorem in Distributed Source Coding (DSC). To the best of our knowledge, this is the first data-driven DSC framework for general distributed code design with deep learning.

Original languageEnglish (US)
Title of host publicationProceedings - DCC 2020
Subtitle of host publicationData Compression Conference
EditorsAli Bilgin, Michael W. Marcellin, Joan Serra-Sagrista, James A. Storer
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages10
ISBN (Electronic)9781728164571
StatePublished - Mar 2020
Event2020 Data Compression Conference, DCC 2020 - Snowbird, United States
Duration: Mar 24 2020Mar 27 2020

Publication series

NameData Compression Conference Proceedings
ISSN (Print)1068-0314


Conference2020 Data Compression Conference, DCC 2020
CountryUnited States

Fingerprint Dive into the research topics of 'DRASIC: Distributed recurrent autoencoder for scalable image compression'. Together they form a unique fingerprint.

  • Cite this

    Diao, E., Ding, J., & Tarokh, V. (2020). DRASIC: Distributed recurrent autoencoder for scalable image compression. In A. Bilgin, M. W. Marcellin, J. Serra-Sagrista, & J. A. Storer (Eds.), Proceedings - DCC 2020: Data Compression Conference (pp. 3-12). [9105700] (Data Compression Conference Proceedings; Vol. 2020-March). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/DCC47342.2020.00008