Skip to main content
SpringerLink
Log in

Performance Improvement of Vector Quantization by Using Threshold

  • Conference paper
Advances in Multimedia Information Processing - PCM 2004 (PCM 2004)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 3333))

Included in the following conference series:

Abstract

Vector quantization (VQ) is an elementary technique for image compression. However, the complexity of searching the nearest codeword in a codebook is time-consuming. In this work, we improve the performance of VQ by adopting the concept of THRESHOLD. Our concept utilizes the positional information to represent the geometric relation within codewords. With the new concept, the lookup procedure only need to calculate Euclidean distance for codewords which are within the threshold, thus sifts candidate codewords easily. Our scheme is simple and suitable for hardware implementation. Moreover, the scheme is a plug-in which can cooperate with existing schemes to further fasten search speed. The effectiveness of the proposed scheme is further demonstrated through experiments. In the experimental results, the proposed scheme can reduce 64% computation with only an extra storage of 512 bytes.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer, Boston (1992)

    MATH  Google Scholar 

  2. Chen, T.S., Chang, C.C.: An Efficient Computation of Euclidean Distances Using Approximated Look-Up Table. IEEE Trans. Circuits Syst. Video Technol. 7, 594–599 (2000)

    Article  Google Scholar 

  3. Davidson, G.A., Cappello, P.R., Gersho, A.: Systolic architectures for vector quantization. IEEE Trans. Acoust., Speech, Signal Processing 36, 1651–1664 (1988)

    Article  MATH  Google Scholar 

  4. Park, H., Prasana, V.K.: Modular VLSI architectures for real-time full-searchbased vector quantization. IEEE Trans. Circuits Syst. Video Technol. 3, 309–317 (1993)

    Article  Google Scholar 

  5. Ramamoorthy, P.A., Potu, B., Tran, T.: Bit-serial VLSI implementation nof vector quantizer for real-time image coding. IEEE Trans. Circuits Syst. 36, 1281–1290 (1989)

    Article  MathSciNet  Google Scholar 

  6. Rizvi, S.A., Nasrabadi, N.M.: An efficient euclidean distance computation for quantization using a truncated look-up table. IEEE Trans. Circuits Syst. Video Technol. 5, 370–371 (1995)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Information Management, I-Shou University, Ta-Hsu Hsiang, Kaohsiung County, 840, Taiwan, R.O.C.

    Hung-Yi Chang

  2. Institute of Computer Science and Information Technology, National Taichung Institute of Technology, Taichung, 404, Taiwan, R.O.C.

    Pi-Chung Wang

  3. Department of Logistics Engineering and Management, National Taichung Institute of Technology, Taichung, 404, Taiwan, R.O.C.

    Rong-Chang Chen

  4. Department of Information Management, Ming-Hsin University of Science and Technology, Hsinchu, 304, Taiwan, R.O.C.

    Shuo-Cheng Hu

Authors
  1. Hung-Yi Chang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Pi-Chung Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Rong-Chang Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Shuo-Cheng Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Information and Communication Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, 113-8656, Tokyo, Japan

    Kiyoharu Aizawa

  2. Tokyo Research Laboratory, IBM Research, 1623-14 Shimo-tsuruma, Yamato, 242-0001, Kanagawa, Japan

    Yuichi Nakamura

  3. National Institute of Informatics, Tokyo, Japan

    Shin’ichi Satoh

Rights and permissions

Reprints and permissions

Copyright information

© 2004 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Chang, HY., Wang, PC., Chen, RC., Hu, SC. (2004). Performance Improvement of Vector Quantization by Using Threshold. In: Aizawa, K., Nakamura, Y., Satoh, S. (eds) Advances in Multimedia Information Processing - PCM 2004. PCM 2004. Lecture Notes in Computer Science, vol 3333. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30543-9_81

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-30543-9_81

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-23985-7

  • Online ISBN: 978-3-540-30543-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation