Neural JPEG: End-to-End Image Compression Leveraging a Standard JPEG Encoder-Decoder

Ankur Mali, Alexander G. Ororbia, Daniel Kifer, C. Lee Giles

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

6 Scopus citations


Recent advances in deep learning have led to superhuman performance across a variety of applications. Recently, these methods have been successfully employed to improve the rate-distortion performance in the task of image compression. However, current methods either use additional post-processing blocks on the decoder end to improve compression or propose an end-to-end compression scheme based on heuris-tics. For the majority of these, the trained deep neural networks (DNNs) are not compatible with standard encoders and would be difficult to deploy on personal com-puters and cellphones. In light of this, we propose a system that learns to improve the encoding performance by enhancing its internal neural representations on both the encoder and decoder ends, an approach we call Neural JPEG. We propose frequency domain pre-editing and post-editing methods to optimize the distribution of the DCT coefficients at both encoder and decoder ends in order to improve the stan-dard compression (JPEG) method. Moreover, we design and integrate a scheme for jointly learning quantization tables within this hybrid neural compression framework.

Original languageEnglish (US)
Title of host publicationProceedings - DCC 2022
Subtitle of host publication2022 Data Compression Conference
EditorsAli Bilgin, Michael W. Marcellin, Joan Serra-Sagrista, James A. Storer
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages1
ISBN (Electronic)9781665478939
StatePublished - 2022
Event2022 Data Compression Conference, DCC 2022 - Snowbird, United States
Duration: Mar 22 2022Mar 25 2022

Publication series

NameData Compression Conference Proceedings
ISSN (Print)1068-0314


Conference2022 Data Compression Conference, DCC 2022
Country/TerritoryUnited States

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications


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