Low Bitrate Image Compression With Discretized Gaussian Mixture Likelihoods

Описание: Authors: Zhengxue Cheng, Heming Sun, Jiro Katto Description: 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 rate constraint. Experimental results have demonstrated that our approach Kattolab achieves 0.9761 in terms of MS-SSIM at the rate constraint of 0.15 bpp during the validation phase.