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Optimization methods for data compre...

Motta, Giovanni.

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Optimization methods for data compression.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Optimization methods for data compression./
作者:	Motta, Giovanni.
面頁冊數:	212 p.
附註:	Adviser: James A. Storer.
Contained By:	Dissertation Abstracts International63-03B.
標題:	Computer Science. -
電子資源:	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.
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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.
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