[proxy] link.springer.com — ← back | site home | direct (HTTPS) ↗ | proxy home | ◑ dark◐ light

Implementation of Lempel-ZIV algorithm for lossless compression using VHDL

Minaldevi K. Tank

Abstract

In computer science and information theory, data compression or source coding is the process of encoding information using fewer bits than an unencoded representation would use, through use of specific encoding schemes. As with any communication, compressed data communication only works when both the sender and receiver of the information understand the encoding scheme. For example, this text makes sense only if the receiver understands that it is intended to be interpreted as characters representing theEnglish language. Similarly, compressed data can only be understood if the decoding method is known by the receiver. Compression is useful because it helps reduce the consumption of expensive resources, such as hard disk space or transmission bandwidth. On the downside, compressed data must be decompressed to be used, and this extra processing may be detrimental to some applications. For instance, a compression scheme for video may require expensive hardware for the video to be decompressed fast enough to be viewed as its being decompressed (the option of decompressing the video in full before watching it may be inconvenient, and requires storage space for the decompressed video). The design of data compression schemes therefore involves trade-offs among various factors, including the degree of compression, the amount of distortion introduced (if using a lossy compression scheme), and the computational resources required to compress and uncompress the data.

Preview

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. A. Gersho and R. M. Gray, Vector Quantization and Signal Compression

  2. D. A. Huffman, “A Method for the Construction of Minimum Redundancy Codes,” Proceedings of the IRE, Vol. 40, pp. 1098–1101, 1952

  3. Ziv and A. Lempel, ``A Universal Algorithm for Sequential Data Compression,’’ IEEE Transactions on Information Theory, Vol. 23, pp. 337–342, 1977

  4. Ziv and A. Lempel, ``Compression of Individual Sequences Via Variable-Rate Coding,’’ IEEE Transactions on Information Theory, Vol. 24, pp. 530–536, 1978

  5. T. A. Welch, ``A Technique for High- Performance Data Compression,’’ Computer, pp. 8–18, 1984

  6. Timothy C. Bell, John G. Cleary, and Ian H. Witten. Text compression. Prentice Hall, 1990

  7. Darrel Hankersson, Greg A. Harris, and Peter D. Johnson Jr.. Introduction to Information Theory and Data Compression. CRC Press, 1997

  8. Jerry Gibson, Toby Berger, Tom Lookabaugh, Rich Baker and David Lindbergh. Digital Compression for Multimedia: Principles & Standards. Morgan Kaufmann, 1998

  9. Gilbert Held and Thomas R. Marshall. Data and Image Compression: Tools and Techniques. Wiley 1996 (4th ed.)

  10. Mark Nelson. The Data Compression Book. M&T Books, 1995

  11. David Salomon. Data Compression: The Complete Refer-ence. Springer Verlag, 1998

  12. VHDL, D. Perry. PrenticeHall,Iindia,1998.  Mc Graw Hill 1995

  13. Xilinx Foundation Series 3.1I,Quick Start. Guide Manual 0401895, US

Download references

Author information

Authors and Affiliations

  1. Digital Electronics, Babasaheb Gawde Institute of Technology, Mumbai, India

    Minaldevi K. Tank (Prof., HOD)

Authors

  1. Minaldevi K. Tank

Editor information

Editors and Affiliations

  1. Babasaheb Gawde Institute of Technology, Mumbai, India

    S. J. Pise

About this paper

Cite this paper

Tank, M.K. (2011). Implementation of Lempel-ZIV algorithm for lossless compression using VHDL. In: Pise, S.J. (eds) Thinkquest~2010. Springer, New Delhi. https://doi.org/10.1007/978-81-8489-989-4_51

Download citation

Keywords

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.