  In this paper, we provide a detailed description on our approach designed for
CVPR 2019 Workshop and Challenge on Learned Image Compression (CLIC). Our
approach mainly consists of two proposals, i.e. deep residual learning for
image compression and sub-pixel convolution as up-sampling operations.
Experimental results have indicated that our approaches, Kattolab, Kattolabv2
and KattolabSSIM, achieve 0.972 in MS-SSIM at the rate constraint of 0.15bpp
with moderate complexity during the validation phase.
