Structured Sparsity Learning for Efficient Learned Image Compression

Existing learned image compression (LIC) methods have achieved outstanding performance, but their deployment on resource-constrained devices is hindered by the high computational complexity and large model storage. Sparsity learning can obtain sparse neural networks by applying regularization term and further achieve model compression by pruning. However, it is difficult to achieve a lightweight LIC network by directly applying sparsity learning and pruning due to unstructured sparsity and limitations of entropy model. In this paper, we propose to add L<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2,1</inf> regularization during the network training for image compression task, which generates structured sparsity at both filter and channel level. We further analyze the effect of entropy model capacity, and adopt filter/channel fixing to achieve the alignment of entropy estimation for actual pruning. Moreover, we utilize incremental regularization to improve the sparsity of network and training stability. Experimental results show that our pruned lightweight model can effectively reduce network parameters by an average of 59.73% at the cost of 1.57% BD-rate increase compared with original hyperprior model.