  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 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 within
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 and efficiently, given the same
complexity. 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, Tecnick-100 and Tecnick-40
datasets show that the proposed scheme outperforms all the leading
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 source code is
available at \url{https://github.com/fengyurenpingsheng}
