  With the rapid development of whole brain imaging technology, a large number
of brain images have been produced, which puts forward a great demand for
efficient brain image compression methods. At present, the most commonly used
compression methods are all based on 3-D wavelet transform, such as JP3D.
However, traditional 3-D wavelet transforms are designed manually with certain
assumptions on the signal, but brain images are not as ideal as assumed. What's
more, they are not directly optimized for compression task. In order to solve
these problems, we propose a trainable 3-D wavelet transform based on the
lifting scheme, in which the predict and update steps are replaced by 3-D
convolutional neural networks. Then the proposed transform is embedded into an
end-to-end compression scheme called iWave3D, which is trained with a large
amount of brain images to directly minimize the rate-distortion loss.
Experimental results demonstrate that our method outperforms JP3D significantly
by 2.012 dB in terms of average BD-PSNR.
