17 research outputs found
n-Channel Asymmetric Entropy-Constrained Multiple-Description Lattice Vector Quantization
This paper is about the design and analysis of an index-assignment (IA) based
multiple-description coding scheme for the n-channel asymmetric case. We use
entropy constrained lattice vector quantization and restrict attention to
simple reconstruction functions, which are given by the inverse IA function
when all descriptions are received or otherwise by a weighted average of the
received descriptions. We consider smooth sources with finite differential
entropy rate and MSE fidelity criterion. As in previous designs, our
construction is based on nested lattices which are combined through a single IA
function. The results are exact under high-resolution conditions and
asymptotically as the nesting ratios of the lattices approach infinity. For any
n, the design is asymptotically optimal within the class of IA-based schemes.
Moreover, in the case of two descriptions and finite lattice vector dimensions
greater than one, the performance is strictly better than that of existing
designs. In the case of three descriptions, we show that in the limit of large
lattice vector dimensions, points on the inner bound of Pradhan et al. can be
achieved. Furthermore, for three descriptions and finite lattice vector
dimensions, we show that the IA-based approach yields, in the symmetric case, a
smaller rate loss than the recently proposed source-splitting approach.Comment: 49 pages, 4 figures. Accepted for publication in IEEE Transactions on
Information Theory, 201
n-Channel Asymmetric Multiple-Description Lattice Vector Quantization
We present analytical expressions for optimal entropy-constrained
multiple-description lattice vector quantizers which, under high-resolutions
assumptions, minimize the expected distortion for given packet-loss
probabilities. We consider the asymmetric case where packet-loss probabilities
and side entropies are allowed to be unequal and find optimal quantizers for
any number of descriptions in any dimension. We show that the normalized second
moments of the side-quantizers are given by that of an -dimensional sphere
independent of the choice of lattices. Furthermore, we show that the optimal
bit-distribution among the descriptions is not unique. In fact, within certain
limits, bits can be arbitrarily distributed.Comment: To appear in the proceedings of the 2005 IEEE International Symposium
on Information Theory, Adelaide, Australia, September 4-9, 200
Real-Time Perceptual Moving-Horizon Multiple-Description Audio Coding
A novel scheme for perceptual coding of audio for robust and real-time communication is designed and analyzed. As an alternative to PCM, DPCM, and more general noise-shaping converters, we propose to use psychoacoustically optimized noise-shaping quantizers based on the moving-horizon principle. In moving-horizon quantization, a few samples look-ahead is allowed at the encoder, which makes it possible to better shape the quantization noise and thereby reduce the resulting distortion over what is possible with conventional noise-shaping techniques. It is first shown that significant gains over linear PCM can be obtained without introducing a delay and without requiring postprocessing at the decoder, i.e., the encoded samples can be stored as, e.g., 16-bit linear PCM on CD-ROMs, and played out on standards-compliant CD players. We then show that multiple-description coding can be combined with moving-horizon quantization in order to combat possible erasures on the wireless link without introducing additional delays
Multiple Description Coding Using A New Bitplane-LVQ Scheme.
In this paper, a novel Bitplane-LVQ technique to compress subbands bitplane coefficients is proposed for multiple description coding (MDC) system
Stabilizing Error Correction Codes for Controlling LTI Systems over Erasure Channels
We propose (k,k') stabilizing codes, which is a type of delayless error
correction codes that are useful for control over networks with erasures. For
each input symbol, k output symbols are generated by the stabilizing code.
Receiving any k' of these outputs guarantees stability. Thus, the system to be
stabilized is taken into account in the design of the erasure codes. Our focus
is on LTI systems, and we construct codes based on independent encodings and
multiple descriptions. The theoretical efficiency and performance of the codes
are assessed, and their practical performances are demonstrated in a simulation
study. There is a significant gain over other delayless codes such as
repetition codes.Comment: Accepted and presented at the IEEE 60th Conference on Decision and
Control (CDC). arXiv admin note: substantial text overlap with
arXiv:2112.1171
Optimal Design of Multiple Description Lattice Vector Quantizers
In the design of multiple description lattice vector quantizers (MDLVQ),
index assignment plays a critical role. In addition, one also needs to choose
the Voronoi cell size of the central lattice v, the sublattice index N, and the
number of side descriptions K to minimize the expected MDLVQ distortion, given
the total entropy rate of all side descriptions Rt and description loss
probability p. In this paper we propose a linear-time MDLVQ index assignment
algorithm for any K >= 2 balanced descriptions in any dimensions, based on a
new construction of so-called K-fraction lattice. The algorithm is greedy in
nature but is proven to be asymptotically (N -> infinity) optimal for any K >=
2 balanced descriptions in any dimensions, given Rt and p. The result is
stronger when K = 2: the optimality holds for finite N as well, under some mild
conditions. For K > 2, a local adjustment algorithm is developed to augment the
greedy index assignment, and conjectured to be optimal for finite N.
Our algorithmic study also leads to better understanding of v, N and K in
optimal MDLVQ design. For K = 2 we derive, for the first time, a
non-asymptotical closed form expression of the expected distortion of optimal
MDLVQ in p, Rt, N. For K > 2, we tighten the current asymptotic formula of the
expected distortion, relating the optimal values of N and K to p and Rt more
precisely.Comment: Submitted to IEEE Trans. on Information Theory, Sep 2006 (30 pages, 7
figures
Multiple-Description Coding by Dithered Delta-Sigma Quantization
We address the connection between the multiple-description (MD) problem and
Delta-Sigma quantization. The inherent redundancy due to oversampling in
Delta-Sigma quantization, and the simple linear-additive noise model resulting
from dithered lattice quantization, allow us to construct a symmetric and
time-invariant MD coding scheme. We show that the use of a noise shaping filter
makes it possible to trade off central distortion for side distortion.
Asymptotically as the dimension of the lattice vector quantizer and order of
the noise shaping filter approach infinity, the entropy rate of the dithered
Delta-Sigma quantization scheme approaches the symmetric two-channel MD
rate-distortion function for a memoryless Gaussian source and MSE fidelity
criterion, at any side-to-central distortion ratio and any resolution. In the
optimal scheme, the infinite-order noise shaping filter must be minimum phase
and have a piece-wise flat power spectrum with a single jump discontinuity. An
important advantage of the proposed design is that it is symmetric in rate and
distortion by construction, so the coding rates of the descriptions are
identical and there is therefore no need for source splitting.Comment: Revised, restructured, significantly shortened and minor typos has
been fixed. Accepted for publication in the IEEE Transactions on Information
Theor