Entropy in Coding Theory is a fundamental measure in information theory that quantifies the average amount of information produced by a stochastic source of data, serving as the theoretical foundation for determining the limits of data compression and the efficiency of coding schemes.
301
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20.1K
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531
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286
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2017–2026
27
Parents
Publications
301
Benjamin Schumacher · Physical Review A · 1995 · 1.1K citations
Second-generation image-coding techniques
M. Kunt, A. Ikonomopoulos, M. Kocher · Proceedings of the IEEE · 1985 · 645 citations
1–5 of 301
Authors
531
- SV
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1–5 of 531
Institutions
286
Technion – Israel Institute of Technology
Haifa, Israel
11 h-index
13 publications
1.1K citations
Massachusetts Institute of Technology
Cambridge, United States
11 h-index
21 publications
2.7K citations
École Polytechnique Fédérale de Lausanne
Lausanne, Switzerland
10 h-index
21 publications
2K citations
California Institute of Technology
Pasadena, United States
9 h-index
11 publications
694 citations
Princeton University
Princeton, United States
8 h-index
11 publications
606 citations
Texas A&M University
College Station, United States
6 h-index
10 publications
385 citations
University of Illinois Urbana-Champaign
Urbana, United States
5 h-index
9 publications
911 citations
University of California San Diego
San Diego, United States
5 h-index
10 publications
947 citations
1–8 of 286
Venues
- IEEE Transactions on Information Theory
85 publications · 9.9K citations
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- Entropy
7 publications · 239 citations
7
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- Electronics Letters
4 publications · 51 citations
4
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- arXiv (Cornell University)
Repository · 4 publications · 149 citations
4
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- Pacific Journal of Mathematics
3 publications · 171 citations
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