Exploring Structural Sparsity in Neural Image Compression [article]

Shanzhi Yin, Chao Li, Wen Tan, Youneng Bao, Yongsheng Liang, Wei Liu
2022 arXiv   pre-print
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
more » ... nce 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.
arXiv:2202.04595v4 fatcat:onlhyxmyffdwhknhvw6hzcmsjy