DeepN-JPEG: A deep neural network favorable JPEG-based image compression framework

Zihao Liu, Tao Liu, Wujie Wen, Lei Jiang, Jie Xu, Yanzhi Wang, Gang Quan

Research output: Chapter in Book/Entry/PoemConference contribution

52 Scopus citations


As one of most fascinating machine learning techniques, deep neural network (DNN) has demonstrated excellent performance in various intelligent tasks such as image classiication. DNN achieves such performance, to a large extent, by performing expensive training over huge volumes of training data. To reduce the data storage and transfer overhead in smart resource-limited Internet-of-Thing (IoT) systems, efective data compression is a "must-have" feature before transferring real-time produced dataset for training or classiication. While there have been many well-known image compression approaches (such as JPEG), we for the irst time ind that a human-visual based image compression approach such as JPEG compression is not an optimized solution for DNN systems, especially with high compression ratios. To this end, we develop an image compression framework tailored for DNN applications, named "DeepN-JPEG", to embrace the nature of deep cascaded information process mechanism of DNN architecture. Extensive experiments, based on "ImageNet" dataset with various state-ofthe- A rt DNNs, show that "DeepN-JPEG" can achieve ∼ 3.5× higher compression rate over the popular JPEG solution while maintaining the same accuracy level for image recognition, demonstrating its great potential of storage and power eiciency in DNN-based smart IoT system design.

Original languageEnglish (US)
Title of host publicationProceedings of the 55th Annual Design Automation Conference, DAC 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Print)9781450357005
StatePublished - Jun 24 2018
Event55th Annual Design Automation Conference, DAC 2018 - San Francisco, United States
Duration: Jun 24 2018Jun 29 2018

Publication series

NameProceedings - Design Automation Conference
VolumePart F137710
ISSN (Print)0738-100X


Other55th Annual Design Automation Conference, DAC 2018
Country/TerritoryUnited States
CitySan Francisco

ASJC Scopus subject areas

  • Computer Science Applications
  • Control and Systems Engineering
  • Electrical and Electronic Engineering
  • Modeling and Simulation


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