Efficient Deep Learning-Based Lossy Image Compression Via Asymmetric Autoencoder and Pruning

Recently, deep learning-based lossy image compression methods have been proposed. However, their efficiency in terms of storage and computational costs has not been addressed adequately. In this paper, we propose efficient lossy image compression methods based on asymmetric autoencoder and decoder pruning. Experimental results demonstrate the effectiveness of our methods.