  Over the past several years, we have witnessed impressive progress in the
field of learned image compression. Recent learned image codecs are commonly
based on autoencoders, that first encode an image into low-dimensional latent
representations and then decode them for reconstruction purposes. To capture
spatial dependencies in the latent space, prior works exploit hyperprior and
spatial context model to build an entropy model, which estimates the bit-rate
for end-to-end rate-distortion optimization. However, such an entropy model is
suboptimal from two aspects: (1) It fails to capture spatially global
correlations among the latents. (2) Cross-channel relationships of the latents
are still underexplored. In this paper, we propose the concept of separate
entropy coding to leverage a serial decoding process for causal contextual
entropy prediction in the latent space. A causal context model is proposed that
separates the latents across channels and makes use of cross-channel
relationships to generate highly informative contexts. Furthermore, we propose
a causal global prediction model, which is able to find global reference points
for accurate predictions of unknown points. Both these two models facilitate
entropy estimation without the transmission of overhead. In addition, we
further adopt a new separate attention module to build more powerful transform
networks. Experimental results demonstrate that our full image compression
model outperforms standard VVC/H.266 codec on Kodak dataset in terms of both
PSNR and MS-SSIM, yielding the state-of-the-art rate-distortion performance.
