Demo Data from EDBT 2026 / reviewers in the wild / expert
Diego Dugatkin
dblp:33/3550
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2004
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Theory of computation · 1
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Theoretical computer science
1 paper |
Coding theory · 100% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Coding theory › source coding › scalable source coding
multiresolution source coding |
0.0 | 1 | 2004 | Multiresolution vector quantization · IEEE Trans. Inf. Theory 2004 |
Coding theory
source coding |
0.0 | 1 | 2004 | Multiresolution vector quantization · IEEE Trans. Inf. Theory 2004 |
Coding theory › source coding › quantization
vector quantization |
0.0 | 1 | 2004 | Multiresolution vector quantization · IEEE Trans. Inf. Theory 2004 |
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2004 | Multiresolution vector quantizationabstractMultiresolution source codes are data compression algorithms yielding embedded source descriptions. The decoder of a multiresolution code can build a source reproduction by decoding the embedded bit stream in part or in whole. All decoding procedures start at the beginning of the binary source description and decode some fraction of that string. Decoding a small portion of the binary string gives a low-resolution reproduction; decoding more yields a higher resolution reproduction; and so on. Multiresolution vector quantizers are block multiresolution source codes. This paper introduces algorithms for designing fixed- and variable-rate multiresolution vector quantizers. Experiments on synthetic data demonstrate performance close to the theoretical performance limit. Experiments on natural images demonstrate performance improvements of up to 8 dB over tree-structured vector quantizers. Some of the lessons learned through multiresolution vector quantizer design lend insight into the design of more sophisticated multiresolution codes. Michelle Effros, Diego Dugatkin |
IEEE Trans. Inf. Theory | 2 |