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.

翻译：神经图像压缩已经达到或超越了传统方法(如JPEG、BPG、WebP),然而,其精密的网络结构带有连锁变迁层,为实际部署带来了沉重的计算负担。在本文中,我们探索神经图像压缩网络的结构宽度,以便在没有任何专门硬件设计或算法的情况下实现实时加速。我们建议使用一个简单的插接适应二进制通道遮罩(ABCM)来判断每个卷发通道的重要性,并在培训中引入宽度。在推断中,不重要的通道被修整,以获得较薄的网络,而较少的计算。我们把方法运用到三个神经图像压缩网络中,使用不同的酶模型来验证其有效性和普遍性,实验结果表明,最多可以以微小的性能下降达到7x计算减速和3x加速。