  One critical component in lossy deep image compression is the entropy model,
which predicts the probability distribution of the quantized latent
representation in the encoding and decoding modules. Previous works build
entropy models upon convolutional neural networks which are inefficient in
capturing global dependencies. In this work, we propose a novel
transformer-based entropy model, termed Entroformer, to capture long-range
dependencies in probability distribution estimation effectively and
efficiently. Different from vision transformers in image classification, the
Entroformer is highly optimized for image compression, including a top-k
self-attention and a diamond relative position encoding. Meanwhile, we further
expand this architecture with a parallel bidirectional context model to speed
up the decoding process. The experiments show that the Entroformer achieves
state-of-the-art performance on image compression while being time-efficient.
