  Recently, learned image compression methods have outperformed traditional
hand-crafted ones including BPG. One of the keys to this success is learned
entropy models that estimate the probability distribution of the quantized
latent representation. Like other vision tasks, most recent learned entropy
models are based on convolutional neural networks (CNNs). However, CNNs have a
limitation in modeling long-range dependencies due to their nature of local
connectivity, which can be a significant bottleneck in image compression where
reducing spatial redundancy is a key point. To overcome this issue, we propose
a novel entropy model called Information Transformer (Informer) that exploits
both global and local information in a content-dependent manner using an
attention mechanism. Our experiments show that Informer improves
rate--distortion performance over the state-of-the-art methods on the Kodak and
Tecnick datasets without the quadratic computational complexity problem. Our
source code is available at https://github.com/naver-ai/informer.
