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Optimization methods for data compre...

Motta, Giovanni.

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Optimization methods for data compression.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Optimization methods for data compression./
Author:	Motta, Giovanni.
Description:	212 p.
Notes:	Adviser: James A. Storer.
Contained By:	Dissertation Abstracts International63-03B.
Subject:	Computer Science. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3045904
ISBN:	0493598979

Optimization methods for data compression.
Motta, Giovanni.

Optimization methods for data compression. - 212 p.

Adviser: James A. Storer.

Thesis (Ph.D.)--Brandeis University, 2002.

Many data compression algorithms use ad-hoc techniques to compress data efficiently. Only in very few cases, can data compressors be proved to achieve optimality on a specific information source, and even in these cases, algorithms often use sub-optimal procedures in their execution.

ISBN: 0493598979Subjects--Topical Terms:

626642
Computer Science.

Optimization methods for data compression.
LDR:02980nam 2200301 a 45 001 933689
005 20110506
008 110506s2002 eng d
020 $a 0493598979
035 $a (UnM)AAI3045904
035 $a AAI3045904
040 $a UnM $c UnM
100 1 $a Motta, Giovanni. $3 1067169
245 1 0 $a Optimization methods for data compression.
300 $a 212 p.
500 $a Adviser: James A. Storer.
500 $a Source: Dissertation Abstracts International, Volume: 63-03, Section: B, page: 1432.
502 $a Thesis (Ph.D.)--Brandeis University, 2002.
520 $a Many data compression algorithms use ad-hoc techniques to compress data efficiently. Only in very few cases, can data compressors be proved to achieve optimality on a specific information source, and even in these cases, algorithms often use sub-optimal procedures in their execution.
520 $a It is appropriate to ask whether the replacement of a sub-optimal strategy by an optimal one in the execution of a given algorithm results in a substantial improvement of its performance. Because of the differences between algorithms the answer to this question is domain dependent and our investigation is based on a case-by-case analysis of the effects of using an optimization procedure in a data compression algorithm.
520 $a The question that we want to answer is how and how much the replacement of a sub-optimal strategy by an optimal one influences the performance of a data compression algorithm. We analyze three algorithms, each in a different domain of data compression: vector quantization, lossless image compression and video coding. Two algorithms are new, introduced by us and one is a widely accepted and well-known standard in video coding to which we apply a novel optimized rate control.
520 $a Besides the contributions consisting of the introduction of two new data compression algorithms that improve the current state of the art, and the introduction of a novel rate control algorithm suitable for video compression, this work is relevant for a number of reasons: (1) A measure of the improvement achievable by an optimal strategy provides powerful insights about the best performance obtainable by a data compression algorithm; (2) As we show in the case of low bit rate video compression, optimal algorithms can frequently be simplified to provide effective heuristics; (3) Existing and new heuristics can be carefully evaluated by comparing their complexity and performance to the characteristics of an optimal solution; (4) Since the empirical entropy of a “natural” data source is always unknown, optimal data compression algorithms provide improved upper bounds on that measure.
590 $a School code: 0021.
650 4 $a Computer Science. $3 626642
690 $a 0984
710 2 0 $a Brandeis University. $3 1017415
773 0 $t Dissertation Abstracts International $g 63-03B.
790 $a 0021
790 1 0 $a Storer, James A., $e advisor
791 $a Ph.D.
792 $a 2002
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3045904