Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: An Autoencoder-based Learned Image Compressor: Description of Challenge
  Proposal by NCTU   We propose a lossy image compression system using the deep-learning
autoencoder structure to participate in the Challenge on Learned Image
Compression (CLIC) 2018. Our autoencoder uses the residual blocks with skip
connections to reduce the correlation among image pixels and condense the input
image into a set of feature maps, a compact representation of the original
image. The bit allocation and bitrate control are implemented by using the
importance maps and quantizer. The importance maps are generated by a separate
neural net in the encoder. The autoencoder and the importance net are trained
jointly based on minimizing a weighted sum of mean squared error, MS-SSIM, and
a rate estimate. Our aim is to produce reconstructed images with good
subjective quality subject to the 0.15 bits-per-pixel constraint.

18
The categories of this paper are: econ
5
The categories of this paper are: gr-qc
36
The categories of this paper are: cmp-lg
16
The categories of this paper are: stat
8
The categories of this paper are: hep-ex
22
The categories of this paper are: chao-dyn
30
The categories of this paper are: q-alg
11
The categories of this paper are: hep-lat
35
The categories of this paper are: mtrl-th
26
The categories of this paper are: bayes-an
13
The categories of this paper are: quant-ph
10
The categories of this paper are: q-bio
19
The categories of this paper are: eess
33
The categories of this paper are: plasm-ph
20
The categories of this paper are: acc-phys
4
The categories of this paper are: cond-mat