  Most learning-based image compression methods lack efficiency for high image
quality due to their non-invertible design. The decoding function of the
frequently applied compressive autoencoder architecture is only an approximated
inverse of the encoding transform. This issue can be resolved by using
invertible latent variable models, which allow a perfect reconstruction if no
quantization is performed. Furthermore, many traditional image and video coders
apply dynamic block partitioning to vary the compression of certain image
regions depending on their content. Inspired by this approach, hierarchical
latent spaces have been applied to learning-based compression networks. In this
paper, we present a novel concept, which adapts the hierarchical latent space
for augmented normalizing flows, an invertible latent variable model. Our best
performing model achieved average rate savings of more than 7% over comparable
single-scale models.
