  Neural image compression have reached or out-performed traditional methods
(such as JPEG, BPG, WebP). However,their sophisticated network structures with
cascaded convolution layers bring heavy computational burden for practical
deployment. In this paper, we explore the structural sparsity in neural image
compression network to obtain real-time acceleration without any specialized
hardware design or algorithm. We propose a simple plug-in adaptive binary
channel masking(ABCM) to judge the importance of each convolution channel and
introduce sparsity during training. During inference, the unimportant channels
are pruned to obtain slimmer network and less computation. We implement our
method into three neural image compression networks with different entropy
models to verify its effectiveness and generalization, the experiment results
show that up to 7x computation reduction and 3x acceleration can be achieved
with negligible performance drop.
