Name
Title | ||
|---|---|---|
A hierarchical dynamic programming approach to fixed-rate, entropy-coded quantization |
Abstract | ||
|---|---|---|
In quantization of any source with a nonuniform probability density function, the entropy coding of the quantizer output can result in a substantial decrease in bit rate. A straightforward entropy coding scheme faces us with the problem of variable data rate. A solution in a space of dimensionality N is to select an appropriate subset of elements in the N-fold Cartesian product of a scalar quantizer and represent its elements with codewords of the same length. The drawback is that the search/addressing of this scheme can no longer be achieved independently along the one-dimensional subspaces. A reasonable rule is to select the N-fold symbols of the highest probability. For a memoryless source, this is equivalent to selecting the N-fold symbols with the lowest additive self-information. In this case, due to the additivity property of the self-information, the selected subset has a high degree of structure which can be used to substantially decrease the search/addressing complexity. A dynamic programming approach is used to exploit this structure. We build our recursive structure required for the dynamic programming in a hierarchy of levels. This results in several benefits over the conventional trellis-based approaches. Using this structure, we develop efficient rules (based on aggregating the states) to substantially reduce the search/addressing complexities while keeping the degradation in performance negligible |
Year | DOI | Venue |
|---|---|---|
1996 | 10.1109/18.508863 | IEEE Transactions on Information Theory |
Keywords | Field | DocType |
hierarchical dynamic programming approach,entropy-coded quantization,appropriate subset,n-fold cartesian product,lowest additive self-information,bit rate,recursive structure,highest probability,dynamic programming approach,dynamic programming,entropy coding,n-fold symbol,decoding,probability density function,probability,degradation,vector quantization,rule based,performance,indexing terms,source coding,cartesian product,lattices,additives | Discrete mathematics,Dynamic programming,Mathematical optimization,Entropy encoding,Computer science,Bit Rate Reduction,Curse of dimensionality,Vector quantization,Decoding methods,Quantization (signal processing),Probability density function | Journal |
Volume | Issue | ISSN |
42 | 4 | 0018-9448 |
Citations | PageRank | References |
2 | 0.39 | 9 |
Authors | ||
1 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| A. K. Khandani | 1 | 297 | 19.65 |