  Variational autoencoders (VAEs) have witnessed great success in performing
the compression of image datasets. This success, made possible by the bits-back
coding framework, has produced competitive compression performance across many
benchmarks. However, despite this, VAE architectures are currently limited by a
combination of coding practicalities and compression ratios. That is, not only
do state-of-the-art methods, such as normalizing flows, often demonstrate
out-performance, but the initial bits required in coding makes single and
parallel image compression challenging. To remedy this, we introduce Split
Hierarchical Variational Compression (SHVC). SHVC introduces two novelties.
Firstly, we propose an efficient autoregressive prior, the autoregressive
sub-pixel convolution, that allows a generalisation between per-pixel
autoregressions and fully factorised probability models. Secondly, we define
our coding framework, the autoregressive initial bits, that flexibly supports
parallel coding and avoids -- for the first time -- many of the practicalities
commonly associated with bits-back coding. In our experiments, we demonstrate
SHVC is able to achieve state-of-the-art compression performance across
full-resolution lossless image compression tasks, with up to 100x fewer model
parameters than competing VAE approaches.

Image and Video Processing;Computer Vision and Pattern Recognition;Machine Learning;Machine Learning