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1991 | Buch

Introductory Survey Kapitel lesen Erstes Kapitel lesen

Adaptive Data Compression

verfasst von: Ross N. Williams

Verlag: Springer US

Buchreihe : The International Series in Engineering and Computer Science

Enthalten in: Professional Book Archive

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Über dieses Buch

Following an exchange of correspondence, I met Ross in Adelaide in June 1988. I was approached by the University of Adelaide about being an external examiner for this dissertation and willingly agreed. Upon receiving a copy of this work, what struck me most was the scholarship with which Ross approaches and advances this relatively new field of adaptive data compression. This scholarship, coupled with the ability to express himself clearly using figures, tables, and incisive prose, demanded that Ross's dissertation be given a wider audience. And so this thesis was brought to the attention of Kluwer. The modern data compression paradigm furthered by this work is based upon the separation of adaptive context modelling, adaptive statistics, and arithmetic coding. This work offers the most complete bibliography on this subject I am aware of. It provides an excellent and lucid review of the field, and should be equally as beneficial to newcomers as to those of us already in the field.

Inhaltsverzeichnis

Frontmatter
Chapter 1. Introductory Survey
Abstract
The purpose of data compression is to remove redundancy from data so that it takes less time to transmit and less space to store. Data compression increases system throughput, improves network security and relieves programmers of the task of packing data efficiently. Because data compression operates at the logical level, it cannot be made obsolete by advances in storage or network technology.
Ross N. Williams
Chapter 2. The DHPC Algorithm
Abstract
Chapter 1 contained a review of the development and current status of data compression. The remainder of the thesis presents the results of the author’s work in this field.
Ross N. Williams
Chapter 3. A Classification of Adaptivity
Abstract
So far, we have reviewed the field of data compression and presented a new Markov algorithm called DHPC. In this chapter we investigate adaptivity in data compression algorithms and describe how various kinds of adaptivity can be incorporated into DHPC. This yields new insights into what is desirable in an adaptive algorithm and into mechanisms for implementing adaptive algorithms in general.
Ross N. Williams
Chapter 4. An Experimental Adaptive Algorithm
Abstract
In this chapter an algorithm is presented that incorporates many of the mechanisms for adaptivity described in Chapter 3. The algorithm began as DHPC and developed incrementally. Originally, the algorithm was to be used to investigate the performance of variants of DHPC and PPM. However, as the algorithm developed, it became clear that the algorithm’s flexibility and integration of diverse, interacting features was of interest in its own right. In this chapter, the algorithm, called the SAKDC83algorithm, is discussed in detail and the results of experiments that explore its parameter space are presented. The exploration of SAKDC’s parameter space not only lends experimental support to the theory presented in Chapter 3, but provides guidelines for practitioners working with Markov algorithms.
Ross N. Williams
Chapter 5. A Multimodal Algorithm
Abstract
Of the classes of adaptivity presented in Chapter 3, asymptotic and local adaptivity are of greatest interest. The flexible SAKDC algorithm described in Chapter 4 incorporates both forms of adaptivity as special cases. In this chapter, both locally adaptive and asymptotically adaptive instances of SAKDC are employed in a single algorithm that combines the best aspects of both.
Ross N. Williams
Chapter 6. Applications to User Interfaces
Abstract
Data compression techniques are used to reduce the volume of data being conveyed through a channel. Applications of data compression are distinguished by the nature of their channel. For data transfer, the channel is a communications line. For data storage, the channel is a storage medium. Other applications, such as authorship identification [Roberts82], which rely on fluctuations in data compression performance, use an imaginary channel. In this chapter we introduce a new applications area whose channel is the interface between a user and a computer terminal.
Ross N. Williams
Chapter 7. Conclusions
Abstract
The goal of this research was to investigate the use of adaptivity in data compression. This goal has been achieved by identifying different kinds of adaptivity, implementing them and evaluating their performance. This chapter presents the highlights of this thesis. It does not summarize it. Summaries appear at the end of each chapter.
Ross N. Williams
Backmatter
Metadaten
Titel
Adaptive Data Compression
verfasst von
Ross N. Williams
Copyright-Jahr
1991
Verlag
Springer US
Electronic ISBN
978-1-4615-4046-5
Print ISBN
978-1-4613-6810-6
DOI
https://doi.org/10.1007/978-1-4615-4046-5