Joint Asymmetric Convolution Block and Local/Global Context Optimization for Learned Image Compression

摘要

Recently, the learned image compression methods have achieved remarkable performance gains. However, existing learned methods lack the mechanism to capture global context for probability density model parameter estimation, or the ability of extracting features to capture spatial correlations where needs to be improved. To resolve these problems, a novel learned image compression framework is proposed in this paper.