  In this paper, we present our image compression framework designed for CLIC
2020 competition. Our method is based on Variational AutoEncoder (VAE)
architecture which is strengthened with residual structures. In short, we make
three noteworthy improvements here. First, we propose a 3-D context entropy
model which can take advantage of known latent representation in current
spatial locations for better entropy estimation. Second, a light-weighted
residual structure is adopted for feature learning during entropy estimation.
Finally, an effective training strategy is introduced for practical adaptation
with different resolutions. Experiment results indicate our image compression
method achieves 0.9775 MS-SSIM on CLIC validation set and 0.9809 MS-SSIM on
test set.
