  In this paper, we provide a detailed description on our submitted method
Kattolab to Workshop and Challenge on Learned Image Compression (CLIC) 2020.
Our method mainly incorporates discretized Gaussian Mixture Likelihoods to
previous state-of-the-art learned compression algorithms. Besides, we also
describes the acceleration strategies and bit optimization with the low-rate
constraint. Experimental results have demonstrated that our approach Kattolab
achieves 0.9761 and 0.9802 in terms of MS-SSIM at the rate constraint of
0.15bpp during the validation phase and test phase, respectively. This project
page is at this https URL
https://github.com/ZhengxueCheng/Learned-Image-Compression-with-GMM-and-Attention
