Lossless Image Compression Using Reversible Integer Wavelet Transforms and Convolutional Neural Networks

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.