  The entropy of the codes usually serves as the rate loss in the recent
learned lossy image compression methods. Precise estimation of the
probabilistic distribution of the codes plays a vital role in the performance.
However, existing deep learning based entropy modeling methods generally assume
the latent codes are statistically independent or depend on some side
information or local context, which fails to take the global similarity within
the context into account and thus hinder the accurate entropy estimation. To
address this issue, we propose a non-local operation for context modeling by
employing the global similarity within the context. Specifically, we first
introduce the proxy similarity functions and spatial masks to handle the
missing reference problem in context modeling. Then, we combine the local and
the global context via a non-local attention block and employ it in masked
convolutional networks for entropy modeling. The entropy model is further
adopted as the rate loss in a joint rate-distortion optimization to guide the
training of the analysis transform and the synthesis transform network in
transforming coding framework. Considering that the width of the transforms is
essential in training low distortion models, we finally produce a U-Net block
in the transforms to increase the width with manageable memory consumption and
time complexity. Experiments on Kodak and Tecnick datasets demonstrate the
superiority of the proposed context-based non-local attention block in entropy
modeling and the U-Net block in low distortion compression against the existing
image compression standards and recent deep image compression models.
