15,816 research outputs found
An Achievable Rate-Distortion Region for the Multiple Descriptions Problem
A multiple-descriptions (MD) coding strategy is proposed and an inner bound
to the achievable rate-distortion region is derived. The scheme utilizes linear
codes. It is shown in two different MD set-ups that the linear coding scheme
achieves a larger rate-distortion region than previously known random coding
strategies. Furthermore, it is shown via an example that the best known random
coding scheme for the set-up can be improved by including additional randomly
generated codebooks
Multiuser Successive Refinement and Multiple Description Coding
We consider the multiuser successive refinement (MSR) problem, where the
users are connected to a central server via links with different noiseless
capacities, and each user wishes to reconstruct in a successive-refinement
fashion. An achievable region is given for the two-user two-layer case and it
provides the complete rate-distortion region for the Gaussian source under the
MSE distortion measure. The key observation is that this problem includes the
multiple description (MD) problem (with two descriptions) as a subsystem, and
the techniques useful in the MD problem can be extended to this case. We show
that the coding scheme based on the universality of random binning is
sub-optimal, because multiple Gaussian side informations only at the decoders
do incur performance loss, in contrast to the case of single side information
at the decoder. We further show that unlike the single user case, when there
are multiple users, the loss of performance by a multistage coding approach can
be unbounded for the Gaussian source. The result suggests that in such a
setting, the benefit of using successive refinement is not likely to justify
the accompanying performance loss. The MSR problem is also related to the
source coding problem where each decoder has its individual side information,
while the encoder has the complete set of the side informations. The MSR
problem further includes several variations of the MD problem, for which the
specialization of the general result is investigated and the implication is
discussed.Comment: 10 pages, 5 figures. To appear in IEEE Transaction on Information
Theory. References updated and typos correcte
Erasure Multiple Descriptions
We consider a binary erasure version of the n-channel multiple descriptions
problem with symmetric descriptions, i.e., the rates of the n descriptions are
the same and the distortion constraint depends only on the number of messages
received. We consider the case where there is no excess rate for every k out of
n descriptions. Our goal is to characterize the achievable distortions D_1,
D_2,...,D_n. We measure the fidelity of reconstruction using two distortion
criteria: an average-case distortion criterion, under which distortion is
measured by taking the average of the per-letter distortion over all source
sequences, and a worst-case distortion criterion, under which distortion is
measured by taking the maximum of the per-letter distortion over all source
sequences. We present achievability schemes, based on random binning for
average-case distortion and systematic MDS (maximum distance separable) codes
for worst-case distortion, and prove optimality results for the corresponding
achievable distortion regions. We then use the binary erasure multiple
descriptions setup to propose a layered coding framework for multiple
descriptions, which we then apply to vector Gaussian multiple descriptions and
prove its optimality for symmetric scalar Gaussian multiple descriptions with
two levels of receivers and no excess rate for the central receiver. We also
prove a new outer bound for the general multi-terminal source coding problem
and use it to prove an optimality result for the robust binary erasure CEO
problem. For the latter, we provide a tight lower bound on the distortion for
\ell messages for any coding scheme that achieves the minimum achievable
distortion for k messages where k is less than or equal to \ell.Comment: 48 pages, 2 figures, submitted to IEEE Trans. Inf. Theor
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