Overhead reduction technique for mega-state compression schemes

A. Bookstein; S. T. Klein; T. Raita

Overhead reduction technique for mega-state compression schemes

A. Bookstein
, S. T. Klein
, T. Raita

The University of Chicago

Research output: Contribution to journal › Conference article › peer-review

2 Scopus citations

Abstract

Many of the most effective compression methods involve complicated models. Unfortunately, as model complexity increases, so does the cost of storing the model itself. This paper examines a method to reduce the amount of storage needed to represent a Markov model with an extended alphabet, by applying a clustering scheme that brings together similar states. Experiments run on a variety of large natural language texts show that much of the overhead of storing the model can be saved at the cost of a very small loss of compression efficiency.

Original language	English
Pages (from-to)	367-376
Number of pages	10
Journal	Data Compression Conference Proceedings
State	Published - 1997
Externally published	Yes
Event	Proceedings of the 1997 Data Compression Conference, DCC'97 - Snowbird, UT, USA Duration: 25 Mar 1997 → 27 Mar 1997

Bibliographical note

Funding Information:
* The work of the first author (AB) was supported, in part, by NSF Grant IRI-9307895-A01. The author gratefully acknowledges this support. We also wish to acknowledge support given by the Academy of Finland to TR, t To whom all correspondence should be addressed: tel: (773) 702-8268, fax: (773) 702-9861, [email protected]; [email protected], and [email protected]

Funding

* The work of the first author (AB) was supported, in part, by NSF Grant IRI-9307895-A01. The author gratefully acknowledges this support. We also wish to acknowledge support given by the Academy of Finland to TR, t To whom all correspondence should be addressed: tel: (773) 702-8268, fax: (773) 702-9861, [email protected]; [email protected], and [email protected]

Funders	Funder number
National Science Foundation	IRI-9307895-A01

Cite this

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title = "Overhead reduction technique for mega-state compression schemes",

abstract = "Many of the most effective compression methods involve complicated models. Unfortunately, as model complexity increases, so does the cost of storing the model itself. This paper examines a method to reduce the amount of storage needed to represent a Markov model with an extended alphabet, by applying a clustering scheme that brings together similar states. Experiments run on a variety of large natural language texts show that much of the overhead of storing the model can be saved at the cost of a very small loss of compression efficiency.",

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note = "Funding Information: * The work of the first author (AB) was supported, in part, by NSF Grant IRI-9307895-A01. The author gratefully acknowledges this support. We also wish to acknowledge support given by the Academy of Finland to TR, t To whom all correspondence should be addressed: tel: (773) 702-8268, fax: (773) 702-9861, [email protected]; [email protected], and [email protected]; Proceedings of the 1997 Data Compression Conference, DCC'97 ; Conference date: 25-03-1997 Through 27-03-1997",

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AU - Klein, S. T.

AU - Raita, T.

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PY - 1997

Y1 - 1997

N2 - Many of the most effective compression methods involve complicated models. Unfortunately, as model complexity increases, so does the cost of storing the model itself. This paper examines a method to reduce the amount of storage needed to represent a Markov model with an extended alphabet, by applying a clustering scheme that brings together similar states. Experiments run on a variety of large natural language texts show that much of the overhead of storing the model can be saved at the cost of a very small loss of compression efficiency.

AB - Many of the most effective compression methods involve complicated models. Unfortunately, as model complexity increases, so does the cost of storing the model itself. This paper examines a method to reduce the amount of storage needed to represent a Markov model with an extended alphabet, by applying a clustering scheme that brings together similar states. Experiments run on a variety of large natural language texts show that much of the overhead of storing the model can be saved at the cost of a very small loss of compression efficiency.

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T2 - Proceedings of the 1997 Data Compression Conference, DCC'97

Y2 - 25 March 1997 through 27 March 1997

ER -

Overhead reduction technique for mega-state compression schemes

Abstract

Bibliographical note

Funding

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Cite this