Name
Abstract | ||
|---|---|---|
Approaching the 1.5329-dB shaping (granular) gain limit in mean-squared error
(MSE) quantization of R^n is important in a number of problems, notably
dirty-paper coding. For this purpose, we start with a binary low-density
generator-matrix (LDGM) code, and construct the quantization codebook by
periodically repeating its set of binary codewords, or them mapped to m-ary
ones with Gray mapping. The quantization algorithm is based on belief
propagation, and it uses a decimation procedure to do the guessing necessary
for convergence. Using the results of a true typical decimator (TTD) as
reference, it is shown that the asymptotic performance of the proposed
quantizer can be characterized by certain monotonicity conditions on the code's
fixed point properties, which can be analyzed with density evolution, and
degree distribution optimization can be carried out accordingly. When the
number of iterations is finite, the resulting loss is made amenable to analysis
through the introduction of a recovery algorithm from ``bad'' guesses, and the
results of such analysis enable further optimization of the pace of decimation
and the degree distribution. Simulation results show that the proposed
LDGM-based quantizer can achieve a shaping gain of 1.4906 dB, or 0.0423 dB from
the limit, and significantly outperforms trellis-coded quantization (TCQ) at a
similar computational complexity. |
Year | Venue | Keywords |
|---|---|---|
2008 | Clinical Orthopaedics and Related Research | performance- complexity tradeoff,index terms—granular gain,shaping,belief propagation,decimation,ldgm,source coding,density evolution,indexing terms,fixed point property,degree distribution,computational complexity,source code,mean square error |
Field | DocType | Volume |
Discrete mathematics,Decimation,Vector quantization,Degree distribution,Fixed point,Quantization (signal processing),Mathematics,Computational complexity theory,Belief propagation,Codebook | Journal | abs/0801.2 |
Citations | PageRank | References |
6 | 0.47 | 31 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Qingchuan Wang | 1 | 13 | 3.12 |
| Chen He | 2 | 100 | 11.38 |