论文标题
低比特率图像压缩，具有离散的高斯混合物的可能性
Low Bitrate Image Compression with Discretized Gaussian Mixture Likelihoods
论文作者
论文摘要
在本文中，我们对我们提交的方法Kattolab进行了详细说明，并在学习图像压缩（CLIC）2020中挑战。我们的方法主要将离散的高斯混合物可能性纳入了以前的先前最先进的学习压缩算法。此外，我们还用低速约束描述了加速策略和位优化。实验结果表明，我们的方法在验证阶段和测试阶段分别以0.15bpp的速率约束，在MS-SSIM方面以MS-SSIM的形式达到0.9761和0.9802。此项目页面在此https url https://github.com/zhengxuecheng/learnenned-image-compression-with-gmm-and-compention
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
