Judit Cinkler

Publication Date

1998

Document Type

Dissertation/Thesis

First Advisor

Kong, Xuan

Degree Name

M.S. (Master of Science)

Legacy Department

Department of Electrical Engineering

LCSH

Data compression (Telecommunication)

Abstract

Near-lossless data compression algorithm and its applications are investigated in this thesis. Near-lossless data compression is an alternative to the lossless compression scheme. The near-lossless compression techniques studied give quantitative measure about the type and the amount of distortion introduced. The near-lossless compression techniques can yield a much higher data compression ratios than the lossless compression techniques. The algorithm proposed in this thesis is a predictive, context-base, graph searched, entropy-coded DPCM (Differential Pulse Code Modulation) technique with a window-based error criterion. The near-lossless data compression algorithm is developed in the context of image compression. An image is compressed in such a way th a t the total error between the original and the coded images over a W x W window around each pixel does not exceed e > 0 in magnitude. This near-lossless error criterion is developed to preserve the image brightness and color. Issues related to practical implementations are discussed in the thesis to solve the zero-state problem. Methods implemented to solve the zero-state problem are: violate the per-pixel error criterion, use lossless coding when zero-state occurs, set the maximum allowed per-pixel error dynamically, limit the number of states at each stage and the ML-algorithm. The near-lossless data compression are applied in two areas: the edge preserving image compression and near-lossless EEG compression. Images are compressed using the near-lossless data compression scheme to preserve the edge information as defined by a Laplacian operator. The EEG signals are compressed based on the predictive models and the sample-to-sample reconstruction errors are limited to a specified range. Compression results obtained for both applications suggest th a t the proposed near-lossless data compression methods are useful in achieving a high compression ratio while preserving the specific information in the original data.

Comments

Includes bibliographical references (pages [125]-127)

Extent

xv, 127 pages

Language

eng

Publisher

Northern Illinois University

Rights Statement

In Copyright

Rights Statement 2

NIU theses are protected by copyright. They may be viewed from Huskie Commons for any purpose, but reproduction or distribution in any format is prohibited without the written permission of the authors.

Media Type

Text

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