  In this paper, we build autoencoder based pipelines for extreme end-to-end
image compression based on Ball\'e's approach, which is the state-of-the-art
open source implementation in image compression using deep learning. We
deepened the network by adding one more hidden layer before each strided
convolutional layer with exactly the same number of down-samplings and
up-samplings. Our approach outperformed Ball\'e's approach, and achieved around
4.0% reduction in bits per pixel (bpp), 0.03% increase in multi-scale
structural similarity (MS-SSIM), and only 0.47% decrease in peak
signal-to-noise ratio (PSNR), It also outperforms all traditional image
compression methods including JPEG2000 and HEIC by at least 20% in terms of
compression efficiency at similar reconstruction image quality. Regarding
encoding and decoding time, our approach takes similar amount of time compared
with traditional methods with the support of GPU, which means it's almost ready
for industrial applications.
