  Adaptive block partitioning is responsible for large gains in current image
and video compression systems. This method is able to compress large stationary
image areas with only a few symbols, while maintaining a high level of quality
in more detailed areas. Current state-of-the-art neural-network-based image
compression systems however use only one scale to transmit the latent space. In
previous publications, we proposed RDONet, a scheme to transmit the latent
space in multiple spatial resolutions. Following this principle, we extend a
state-of-the-art compression network by a second hierarchical latent-space
level to enable multi-scale processing. We extend the existing rate variability
capabilities of RDONet by a gain unit. With that we are able to outperform an
equivalent traditional autoencoder by 7% rate savings. Furthermore, we show
that even though we add an additional latent space, the complexity only
increases marginally and the decoding time can potentially even be decreased.
