Toward Joint Image Generation and Compression using Generative Adversarial Networks

Understand

In this paper, we present a generative adversarial network framework that generates compressed images instead of synthesizing raw RGB images and compressing them separately.

• In the real world, most images and videos are stored and transferred in a compressed format to save storage capacity and data transfer bandwidth.
• However, since typical generative adversarial networks generate raw RGB images, those generated images need to be compressed by a post-processing stage to reduce the data size.
• Among image compression methods, JPEG has been one of the most commonly used lossy compression methods for still images.