  Convolutional autoencoders are now at the forefront of image compression
research. To improve their entropy coding, encoder output is typically analyzed
with a second autoencoder to generate per-variable parametrized prior
probability distributions. We instead propose a compression scheme that uses a
single convolutional autoencoder and multiple learned prior distributions
working as a competition of experts. Trained prior distributions are stored in
a static table of cumulative distribution functions. During inference, this
table is used by an entropy coder as a look-up-table to determine the best
prior for each spatial location. Our method offers rate-distortion performance
comparable to that obtained with a predicted parametrized prior with only a
fraction of its entropy coding and decoding complexity.
