Sparse Flow Adversarial Model For Robust Image Compression

Abstract

Existing learned-based image compression methods have shown impressive performance. However, most of them rely on the consistency of distribution between training images and test images, which limits the robustness of the trained model. In this paper, we propose a novel compression method called sparse flow adversarial model (SFAM). SFAM employs a deep generative framework to learn a reversible and stable mapping between image distributions, thus it can work in varied scenes for robust compression. Moreover, a sparse adversarial map is introduced into SFAM, to constrain the SFAM to generate more sparser features for efficient compression. Extensive experiments are conducted on different datasets, in which the effectiveness and robustness of the proposed method is verified. Meanwhile, SFAM is trained only once and it can work well on three different datasets, which also proves the robustness of the proposed SFAM.