A new end-to-end image compression system based on convolutional neural networks

Pinar Akyazi; Touradj Ebrahimi

doi:10.1117/12.2530195

6 September 2019 A new end-to-end image compression system based on convolutional neural networks

Pinar Akyazi, Touradj Ebrahimi

Proceedings Volume 11137, Applications of Digital Image Processing XLII; 111370M (2019) https://doi.org/10.1117/12.2530195
Event: SPIE Optical Engineering + Applications, 2019, San Diego, California, United States

Abstract

In this paper, two new end-to-end image compression architectures based on convolutional neural networks are presented. The proposed networks employ 2D wavelet decomposition as a preprocessing step before training and extract features for compression from wavelet coefficients. Training is performed end-to-end and multiple models operating at di↵erent rate points are generated by using a regularizer in the loss function. Results show that the proposed methods outperform JPEG compression, reduce blocking and blurring artifacts, and preserve more details in the images especially at low bitrates.

Conference Presentation

Citation Download Citation

Pinar Akyazi and Touradj Ebrahimi "A new end-to-end image compression system based on convolutional neural networks", Proc. SPIE 11137, Applications of Digital Image Processing XLII, 111370M (6 September 2019); https://doi.org/10.1117/12.2530195

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