  Recent research has shown a strong theoretical connection between variational
autoencoders (VAEs) and the rate-distortion theory. Motivated by this, we
consider the problem of lossy image compression from the perspective of
generative modeling. Starting with ResNet VAEs, which are originally designed
for data (image) distribution modeling, we redesign their latent variable model
using a quantization-aware posterior and prior, enabling easy quantization and
entropy coding at test time. Along with improved neural network architecture,
we present a powerful and efficient model that outperforms previous methods on
natural image lossy compression. Our model compresses images in a
coarse-to-fine fashion and supports parallel encoding and decoding, leading to
fast execution on GPUs. Code is available at
https://github.com/duanzhiihao/lossy-vae.
