Abstract
With a customized characterization of types, every universal one-to-one coding algorithm can be described as follows: assign sequences to binary strings based on their type class sizes from smallest to largest. With this view, the universal coding problem is to optimally characterize types. In this paper, this Type Size approach is studied for universal source coding of an exponential family of distributions, using the most natural type class definition: two sequences are in the same type class if and only if they are indistinguishable in the sense that they have the same probability for every distribution in the family. This characterization is called the point type class. Exact third-order coding rate is derived for the resulting compression algorithm, revealing that the point type approach, while natural, is sub-optimal compared to the quantized type method, which was previously proposed by the authors.
| Original language | English (US) |
|---|---|
| Title of host publication | 2017 51st Annual Conference on Information Sciences and Systems, CISS 2017 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| ISBN (Electronic) | 9781509047802 |
| DOIs | |
| State | Published - May 10 2017 |
| Event | 51st Annual Conference on Information Sciences and Systems, CISS 2017 - Baltimore, United States Duration: Mar 22 2017 → Mar 24 2017 |
Other
| Other | 51st Annual Conference on Information Sciences and Systems, CISS 2017 |
|---|---|
| Country | United States |
| City | Baltimore |
| Period | 3/22/17 → 3/24/17 |
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Keywords
- Data compression
- Fine asymptotics
- Finite blocklength analysis
- Universal algorithm
ASJC Scopus subject areas
- Signal Processing
- Information Systems and Management
- Computer Networks and Communications
- Information Systems
Cite this
Universal coding with point type classes. / Iri, Nematollah; Kosut, Oliver.
2017 51st Annual Conference on Information Sciences and Systems, CISS 2017. Institute of Electrical and Electronics Engineers Inc., 2017. 7926131.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Universal coding with point type classes
AU - Iri, Nematollah
AU - Kosut, Oliver
PY - 2017/5/10
Y1 - 2017/5/10
N2 - With a customized characterization of types, every universal one-to-one coding algorithm can be described as follows: assign sequences to binary strings based on their type class sizes from smallest to largest. With this view, the universal coding problem is to optimally characterize types. In this paper, this Type Size approach is studied for universal source coding of an exponential family of distributions, using the most natural type class definition: two sequences are in the same type class if and only if they are indistinguishable in the sense that they have the same probability for every distribution in the family. This characterization is called the point type class. Exact third-order coding rate is derived for the resulting compression algorithm, revealing that the point type approach, while natural, is sub-optimal compared to the quantized type method, which was previously proposed by the authors.
AB - With a customized characterization of types, every universal one-to-one coding algorithm can be described as follows: assign sequences to binary strings based on their type class sizes from smallest to largest. With this view, the universal coding problem is to optimally characterize types. In this paper, this Type Size approach is studied for universal source coding of an exponential family of distributions, using the most natural type class definition: two sequences are in the same type class if and only if they are indistinguishable in the sense that they have the same probability for every distribution in the family. This characterization is called the point type class. Exact third-order coding rate is derived for the resulting compression algorithm, revealing that the point type approach, while natural, is sub-optimal compared to the quantized type method, which was previously proposed by the authors.
KW - Data compression
KW - Fine asymptotics
KW - Finite blocklength analysis
KW - Universal algorithm
UR - http://www.scopus.com/inward/record.url?scp=85020207174&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85020207174&partnerID=8YFLogxK
U2 - 10.1109/CISS.2017.7926131
DO - 10.1109/CISS.2017.7926131
M3 - Conference contribution
AN - SCOPUS:85020207174
BT - 2017 51st Annual Conference on Information Sciences and Systems, CISS 2017
PB - Institute of Electrical and Electronics Engineers Inc.
ER -