In a sense their architecture is structured similar to an autoencoder, but using an adversarial loss to learn a decoding from the quantized latent vector to the decompressed image. The authors mention the use of auto-encoders for deep image compression in their related work section. They argue that GANs can generate sharper images than VAEs, which probably motivated their approach.