  Image compression is a fundamental research field and many well-known
compression standards have been developed for many decades. Recently, learned
compression methods exhibit a fast development trend with promising results.
However, there is still a performance gap between learned compression
algorithms and reigning compression standards, especially in terms of widely
used PSNR metric. In this paper, we explore the remaining redundancy of recent
learned compression algorithms. We have found accurate entropy models for rate
estimation largely affect the optimization of network parameters and thus
affect the rate-distortion performance. Therefore, in this paper, we propose to
use discretized Gaussian Mixture Likelihoods to parameterize the distributions
of latent codes, which can achieve a more accurate and flexible entropy model.
Besides, we take advantage of recent attention modules and incorporate them
into network architecture to enhance the performance. Experimental results
demonstrate our proposed method achieves a state-of-the-art performance
compared to existing learned compression methods on both Kodak and
high-resolution datasets. To our knowledge our approach is the first work to
achieve comparable performance with latest compression standard Versatile Video
Coding (VVC) regarding PSNR. More importantly, our approach generates more
visually pleasant results when optimized by MS-SSIM. This project page is at
this https URL
https://github.com/ZhengxueCheng/Learned-Image-Compression-with-GMM-and-Attention
