Recently deep learning-based image compression methods have achieved
significant achievements and gradually outperformed traditional approaches
including the latest standard Versatile Video Coding (VVC) in both PSNR and
MS-SSIM metrics. Two key components of learned image compression frameworks are
the entropy model of the latent representations and the encoding/decoding
network architectures. Various models have been proposed, such as
autoregressive, softmax, logistic mixture, Gaussian mixture, and Laplacian.
Existing schemes only use one of these models. However, due to the vast
diversity of images, it is not optimal to use one model for all images, even
different regions of one image. In this paper, we propose a more flexible
discretized Gaussian-Laplacian-Logistic mixture model (GLLMM) for the latent
representations, which can adapt to different contents in different images and
different regions of one image more accurately. Besides, in the
encoding/decoding network design part, we propose a concatenated residual
blocks (CRB), where multiple residual blocks are serially connected with
additional shortcut connections. The CRB can improve the learning ability of
the network, which can further improve the compression performance.
Experimental results using the Kodak and Tecnick datasets show that the
proposed scheme outperforms all the state-of-the-art learning-based methods and
existing compression standards including VVC intra coding (4:4:4 and 4:2:0) in
terms of the PSNR and MS-SSIM. The project page is at
https://github.com/fengyurenpingsheng/Learned-image-compression-with-GLLMM

该论文介绍了一种更灵活的离散化高斯 - 拉普拉斯 - 逻辑混合模型（GLLMM）和串联残差块（CRB）用于深度学习图像压缩，它比传统方法和最新的标准 Versatile Video Coding（VVC）在 PSNR 和 MS-SSIM 度量中效果更好。