  Lossless image compression is an important technique for image storage and
transmission when information loss is not allowed. With the fast development of
deep learning techniques, deep neural networks have been used in this field to
achieve a higher compression rate. Methods based on pixel-wise autoregressive
statistical models have shown good performance. However, the sequential
processing way prevents these methods to be used in practice. Recently,
multi-scale autoregressive models have been proposed to address this
limitation. Multi-scale approaches can use parallel computing systems
efficiently and build practical systems. Nevertheless, these approaches
sacrifice compression performance in exchange for speed. In this paper, we
propose a multi-scale progressive statistical model that takes advantage of the
pixel-wise approach and the multi-scale approach. We developed a flexible
mechanism where the processing order of the pixels can be adjusted easily. Our
proposed method outperforms the state-of-the-art lossless image compression
methods on two large benchmark datasets by a significant margin without
degrading the inference speed dramatically.
