  Precise estimation of the probabilistic structure of natural images plays an
essential role in image compression. Despite the recent remarkable success of
end-to-end optimized image compression, the latent codes are usually assumed to
be fully statistically factorized in order to simplify entropy modeling.
However, this assumption generally does not hold true and may hinder
compression performance. Here we present context-based convolutional networks
(CCNs) for efficient and effective entropy modeling. In particular, a 3D zigzag
scanning order and a 3D code dividing technique are introduced to define proper
coding contexts for parallel entropy decoding, both of which boil down to place
translation-invariant binary masks on convolution filters of CCNs. We
demonstrate the promise of CCNs for entropy modeling in both lossless and lossy
image compression. For the former, we directly apply a CCN to the binarized
representation of an image to compute the Bernoulli distribution of each code
for entropy estimation. For the latter, the categorical distribution of each
code is represented by a discretized mixture of Gaussian distributions, whose
parameters are estimated by three CCNs. We then jointly optimize the CCN-based
entropy model along with analysis and synthesis transforms for rate-distortion
performance. Experiments on the Kodak and Tecnick datasets show that our
methods powered by the proposed CCNs generally achieve comparable compression
performance to the state-of-the-art while being much faster.
