@inproceedings{cb9b053e825045ebb7c93c8e622ca858,
title = "Lossless image compression using reversible integer wavelet transforms and convolutional neural networks",
abstract = "In this work we introduce a lossless compression framework which incorporates convolutional neural networks (CNN) for wavelet subband prediction. A CNN is trained to predict detail coefficients from corresponding approximation coefficients, prediction error is then coded in place of wavelet coefficients. At decompression an identical CNN is used to reproduce the prediction and combine with the decoded residuals for perfect reconstruction of wavelet subbands.",
keywords = "cnn, compression, convolutional neural network, deep learning, wavelets",
author = "Eze Ahanonu and Michael Marcellin and Ali Bilgin",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 Data Compression Conference, DCC 2018 ; Conference date: 27-03-2018 Through 30-03-2018",
year = "2018",
month = jul,
day = "19",
doi = "10.1109/DCC.2018.00048",
language = "English (US)",
series = "Data Compression Conference Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "395",
editor = "Ali Bilgin and Storer, {James A.} and Joan Serra-Sagrista and Marcellin, {Michael W.}",
booktitle = "Proceedings - DCC 2018",
}