1 research outputs found
Multiple Description Convolutional Neural Networks for Image Compression
Multiple description coding (MDC) is able to stably transmit the signal in
the un-reliable and non-prioritized networks, which has been broadly studied
for several decades. However, the traditional MDC doesn't well leverage image's
context features to generate multiple descriptions. In this paper, we propose a
novel standard-compliant convolutional neural network-based MDC framework in
term of image's context features. Firstly, multiple description generator
network (MDGN) is designed to produce appearance-similar yet feature-different
multiple descriptions automatically according to image's content, which are
compressed by standard codec. Secondly, we present multiple description
reconstruction network (MDRN) including side reconstruction network (SRN) and
central reconstruction network (CRN). When any one of two lossy descriptions is
received at the decoder, SRN network is used to improve the quality of this
decoded lossy description by removing the compression artifact and up-sampling
simultaneously. Meanwhile, we utilize CRN network with two decoded descriptions
as inputs for better reconstruction, if both of lossy descriptions are
available. Thirdly, multiple description virtual codec network (MDVCN) is
proposed to bridge the gap between MDGN network and MDRN network in order to
train an end-to-end MDC framework. Here, two learning algorithms are provided
to train our whole framework. In addition to structural similarity loss
function, the produced descriptions are used as opposing labels with multiple
description distance loss function to regularize the training of MDGN network.
These losses guarantee that the generated description images are structurally
similar yet finely diverse. Experimental results show a great deal of objective
and subjective quality measurements to validate the efficiency of the proposed
method.Comment: 13 pages, 3 tables, and 6 figures. Accepted by IEEE Trans. CSVT. Our
another MDC paper based on deep learning is "Deep Multiple Description Coding
by Learning Scalar Quantization", which was accepted by DCC-2019.
(arXiv:1811.01504