Layered Conceptual Image Compression Via Deep Semantic Synthesis

Motivated by the insight of Marr on generative image representations, we propose a layered conceptual image compression scheme by integrating the advantages of both variational auto-encoders (VAEs) and generative adversarial networks (GANs). In particular, the image is represented by two layers: the low-dimensional codes of the stochastic textures encoded by the VAE and the geometric structures characterized by edge maps. Subsequently, the edge maps and latent codes are compressed individually such that the final bit streams are formed in a combined manner. At the decoder side, the GAN synthesizes the decoded images on the basis of the latent codes and the reconstructed edge maps. Experimental results demonstrate that our proposed scheme achieves better visual reconstruction quality than the traditional image compression algorithms such as JPEG, JPEG2000 and HEVC (intra coding) in the low bit rate coding scenarios.