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Image and Text Compression

  • Book
  • © 1992

Overview

Editors:
  1. James A. Storer

    1. Brandeis University, USA

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Part of the book series: The Springer International Series in Engineering and Computer Science (SECS, volume 176)

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Table of contents (10 chapters)

  1. Front Matter

    Pages i-viii
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  2. Image Compression

    1. Front Matter

      Pages 1-1
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    2. Image Compression and Tree-Structured Vector Quantization

      • Robert M. Gray, Pamela C. Cosman, Eve A. Riskin
      Pages 3-34

    3. Fractal Image Compression Using Iterated Transforms

      • Y. Fisher, E. W. Jacobs, R. D. Boss
      Pages 35-61

    4. Optical Techniques for Image Compression*

      • John H. Reif, Akitoshi Yoshida
      Pages 63-82

  3. Text Compression

    1. Front Matter

      Pages 83-83
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    2. Practical Implementations of Arithmetic Coding

      • Paul G. Howard, Jeffrey Scott Vitter
      Pages 85-112

    3. Context Modeling for Text Compression

      • Daniel S. Hirschberg†, Debra A. Lelewer‡
      Pages 113-144

    4. Ziv-Lempel Compressors with Deferred-Innovation

      • Martin Cohn
      Pages 145-157

    5. Massively Parallel Systolic Algorithms for Real-Time Dictionary-Based Text Compression

      • James A. Storer
      Pages 159-178

  4. Coding Theory

    1. Front Matter

      Pages 179-179
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    2. Variations on a Theme by Gallager

      • Renato M. Capocelli, Alfredo De Santis
      Pages 181-213

    3. On the Coding Delay of a General Coder

      • Marcelo J. Weinberger, Abraham Lempel, Jacob Ziv
      Pages 215-252

    4. Finite State Two-Dimensional Compressibility

      • Dafna Sheinwald
      Pages 253-275

  5. Back Matter

    Pages 277-354
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Keywords

About this book

James A. Storer Computer Science Dept. Brandeis University Waltham, MA 02254 Data compression is the process of encoding a body of data to reduce stor­ age requirements. With Lossless compression, data can be decompressed to be identical to the original, whereas with lossy compression, decompressed data may be an acceptable approximation (according to some fidelity criterion) to the original. For example, with digitized video, it may only be necessary that the decompressed video look as good as the original to the human eye. The two primary functions of data compression are: Storage: The capacity of a storage device can be effectively increased with data compression software or hardware that compresses a body of data on its way to the storage device and decompress it when it is retrieved. Communications: The bandwidth of a digital communication link can be effectively increased by compressing data at the sending end and decom­ pressing data at the receiving end. Here it can be crucial that compression and decompression can be performed in real time.

Editors and Affiliations

  • Brandeis University, USA

    James A. Storer

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