Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: End-to-end optimized image compression with competition of prior
  distributions   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.

35
The categories of this paper are: mtrl-th
17
The categories of this paper are: q-fin
22
The categories of this paper are: chao-dyn
33
The categories of this paper are: plasm-ph
12
The categories of this paper are: nucl-th
37
The categories of this paper are: supr-con
11
The categories of this paper are: hep-lat
25
The categories of this paper are: patt-sol
28
The categories of this paper are: alg-geom
16
The categories of this paper are: stat
7
The categories of this paper are: hep-th
2
The categories of this paper are: math
5
The categories of this paper are: gr-qc
27
The categories of this paper are: comp-gas
21
The categories of this paper are: adap-org
32
The categories of this paper are: atom-ph