  We propose a lossy image compression system using the deep-learning
autoencoder structure to participate in the Challenge on Learned Image
Compression (CLIC) 2018. Our autoencoder uses the residual blocks with skip
connections to reduce the correlation among image pixels and condense the input
image into a set of feature maps, a compact representation of the original
image. The bit allocation and bitrate control are implemented by using the
importance maps and quantizer. The importance maps are generated by a separate
neural net in the encoder. The autoencoder and the importance net are trained
jointly based on minimizing a weighted sum of mean squared error, MS-SSIM, and
a rate estimate. Our aim is to produce reconstructed images with good
subjective quality subject to the 0.15 bits-per-pixel constraint.
