The work done by Google Engineers here shows that this neural architecture can result in an even better compression scheme that still allows for higher quality.

Summary: We introduce an architecture that uses a new variant of the Gated Recurrent Unit (a type of RNN that allows units to save activations and process sequences) called Residual Gated Recurrent Unit (Residual GRU). Our Residual GRU combines existing GRUs with the residual connections introduced in "Deep Residual Learning for Image Recognition" to achieve significant image quality gains for a given compression rate. Instead of using a DCT to generate a new bit representation like many compression schemes in use today, we train two sets of neural networks - one to create the codes from the image (encoder) and another to create the image from the codes (decoder).

Left: Original image (1419 KB PNG) at ~1.0 MS-SSIM. Center: JPEG (33 KB) at ~0.9 MS-SSIM. Right: Residual GRU (24 KB) at ~0.9 MS-SSIM. This is 25% smaller for a comparable image quality