  Recently, learned image compression has achieved remarkable performance. The
entropy model, which estimates the distribution of the latent representation,
plays a crucial role in boosting rate-distortion performance. However, most
entropy models only capture correlations in one dimension, while the latent
representation contain channel-wise, local spatial, and global spatial
correlations. To tackle this issue, we propose the Multi-Reference Entropy
Model (MEM) and the advanced version, MEM$^+$. These models capture the
different types of correlations present in latent representation. Specifically,
We first divide the latent representation into slices. When decoding the
current slice, we use previously decoded slices as context and employ the
attention map of the previously decoded slice to predict global correlations in
the current slice. To capture local contexts, we introduce two enhanced
checkerboard context capturing techniques that avoids performance degradation.
Based on MEM and MEM$^+$, we propose image compression models MLIC and
MLIC$^+$. Extensive experimental evaluations demonstrate that our MLIC and
MLIC$^+$ models achieve state-of-the-art performance, reducing BD-rate by
$8.05\%$ and $11.39\%$ on the Kodak dataset compared to VTM-17.0 when measured
in PSNR. Our code is available at https://github.com/JiangWeibeta/MLIC.

Image and Video Processing;Computer Vision and Pattern Recognition