Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning a Virtual Codec Based on Deep Convolutional Neural Network to
  Compress Image   Although deep convolutional neural network has been proved to efficiently
eliminate coding artifacts caused by the coarse quantization of traditional
codec, it's difficult to train any neural network in front of the encoder for
gradient's back-propagation. In this paper, we propose an end-to-end image
compression framework based on convolutional neural network to resolve the
problem of non-differentiability of the quantization function in the standard
codec. First, the feature description neural network is used to get a valid
description in the low-dimension space with respect to the ground-truth image
so that the amount of image data is greatly reduced for storage or
transmission. After image's valid description, standard image codec such as
JPEG is leveraged to further compress image, which leads to image's great
distortion and compression artifacts, especially blocking artifacts, detail
missing, blurring, and ringing artifacts. Then, we use a post-processing neural
network to remove these artifacts. Due to the challenge of directly learning a
non-linear function for a standard codec based on convolutional neural network,
we propose to learn a virtual codec neural network to approximate the
projection from the valid description image to the post-processed compressed
image, so that the gradient could be efficiently back-propagated from the
post-processing neural network to the feature description neural network during
training. Meanwhile, an advanced learning algorithm is proposed to train our
deep neural networks for compression. Obviously, the priority of the proposed
method is compatible with standard existing codecs and our learning strategy
can be easily extended into these codecs based on convolutional neural network.
Experimental results have demonstrated the advances of the proposed method as
compared to several state-of-the-art approaches, especially at very low
bit-rate.

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The categories of this paper are: mtrl-th
9
The categories of this paper are: nlin
18
The categories of this paper are: econ
5
The categories of this paper are: gr-qc
24
The categories of this paper are: dg-ga
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The categories of this paper are: cmp-lg
22
The categories of this paper are: chao-dyn
3
The categories of this paper are: physics
26
The categories of this paper are: bayes-an
29
The categories of this paper are: funct-an
8
The categories of this paper are: hep-ex
2
The categories of this paper are: math
15
The categories of this paper are: nucl-ex
20
The categories of this paper are: acc-phys
17
The categories of this paper are: q-fin
34
The categories of this paper are: chem-ph