Entropy-constrained Quantization
Entropy-Constrained Quantization is a rate-distortion optimization framework in lossy data compression that characterizes a quantizer designed to minimize distortion subject to a constraint on the output entropy, thereby explicitly balancing fidelity and compression efficiency.
117
Publications
5.9K
Citations
153
Authors
93
Institutions
Publications per year
2017–2026
5
Variants
Ecq
117
1–5 of 117
153
11
h-index
9
h-index
8
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6
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1–5 of 153
93
University of Arizona
Tucson, United States
11 h-index
25 publications
883 citations
Stanford University
Stanford, United States
9 h-index
15 publications
1K citations
AT&T (United States)
Bedminster, United States
7 h-index
7 publications
989 citations
Rensselaer Polytechnic Institute
Troy, United States
7 h-index
11 publications
940 citations
Queen's University
Kingston, Canada
7 h-index
9 publications
458 citations
University of Maryland, College Park
College Park, United States
7 h-index
10 publications
268 citations
University of California, Santa Barbara
Santa Barbara, United States
6 h-index
12 publications
279 citations

Delft University of Technology
Delft, The Netherlands
5 h-index
10 publications
278 citations
1–8 of 93
33 publications · 2.7K citations
25
h-index
12 publications · 470 citations
11
h-index
4 publications · 68 citations
4
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4 publications · 48 citations
4
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4 publications · 156 citations
4
h-index
1–5