Skip to main content
SpringerLink
Log in
Book cover

Genetic and Evolutionary Computing pp 197–206Cite as

A Quality Improving Scheme for VQ Decompressed Image Based on DCT

  • Conference paper

  • 1520 Accesses

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 329))

Abstract

Data compression has been at an important stage, which not only needs to achieve higher compression ration but also needs to achieve the low distortion rate. High compression can make us be able to save the same data with smaller space; it can also be save the bandwidth of data transmission on networks. The proposed method is tried to further reduce the size of digital image and improve the visual quality of the decompressed image. The experimental results shows that the proposed method has better visual quality than VQ in case of AC codebook size is greater than 1024. On the other hand, the compression rate of VQ is 0.06 and the proposed method is 0.04 when AC codebook size set as 1024.

This is a preview of subscription content, 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   129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   169.99
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

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Chou, Y.C., Lo, Y.H., Shen, J.J.: A New Quality Improving Scheme for VQ Decompressed Images Based on DWT. Journal of Electronic Science and Technology 11(1), 51–57 (2013)

    Google Scholar 

  2. Tsekouras, G.E.: A Fuzzy Vector Quantization Approach to Image Compression. Applied Mathematics and Computation 167, 539–560 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  3. Xu, W., Nandi, A.K., Zhang, J.: Novel Fuzzy Reinforced Learning Vector Quantisation Algorithm and Its Application in Image Compression. In: IEEE Proceedings Vision, Image, and Signal Processing, vol. 150, pp. 292–298 (2003)

    Google Scholar 

  4. Chen, S., He, Z., Luk, B.L.: A Generic Postprocessing Technique for Image Compression. IEEE Transactions on Circuits and Systems for Video Technology 11, 546–553 (2001)

    Article  Google Scholar 

  5. Lancini, R., Tubaro, S.: Adaptive Vector Quantization for Picture Coding Using Neural Networks. IEEE Transactions on Communications 43, 534–544 (1995)

    Article  MATH  Google Scholar 

  6. Shen, J.J., Huang, H.C.: An Adaptive Image Compression Method Based on Vector Quantization. In: Proceedings of the 1st International Conference Pervasive Computing Signal Processing and Applications, Harbin, China, pp. 377–381 (2010)

    Google Scholar 

  7. Shen, J.J., Lo, Y.H.: A New Approach of Image Compression Based on Difference Vector Quantization. In: Proceedings of the 7th International Conference Intelligent Information Hiding and Multimedia Signal Processing, Dalian, China, pp. 137–140 (2011)

    Google Scholar 

  8. Qian, S.E.: Hyperspectral Data Compression Using a FastVector Quantization Algorithm. IEEE Transactions on Geoscience and Remote Sensing 42(8), 1791–1798 (2004)

    Article  Google Scholar 

  9. Liu, Y.C., Lee, G.H., Taur, J., Tao, C.W.: Index Compression for Vector Quantisation Using Modified Coding Tree Assignment Scheme. IET Image Processing 8(3), 173–182 (2014)

    Article  Google Scholar 

  10. Chou, P.H., Meng, T.H.: Vertex Data Compression through Vector Quantization. IEEE Transactions on Visualization and Computer Graphics 8(4), 373–382 (2002)

    Article  Google Scholar 

  11. Shen, G., Liou, M.L.: An Efficient Codebook Post-Processing Technique and a Window-Based Fast-Search Algorithm for Image Vector Quantization. IEEE Transactions on Circuits and Systems for Video Technology 10(6), 990–997 (2000)

    Article  Google Scholar 

  12. Bagheri Zadeh, P., Buggy, T., Sheikh Akbari, A.: Statistical, DCT and Vector Quantisation-based Video Codec. IET Image Processing 2(3), 107–115 (2008)

    Article  Google Scholar 

  13. Bayer, F.M., Cintra, R.J.: Image Compression Via a Fast DCT Approximation. IEEE Latin America Transactions 8(6), 708–713 (2010)

    Article  Google Scholar 

  14. Ponomarenko, N.N., Egiazarian, K.O., Lukin, V.V., Astola, J.T.: High-Quality DCT-Based Image Compression Using Partition Schemes. IEEE Signal Processing Letters 14(2), 105–108 (2007)

    Article  Google Scholar 

  15. Zhao, D., Gao, W., Chan, Y.K.: Morphological Representation of DCT Coefficients for Image Compression. IEEE Transactions on Circuits and Systems for Video Technology 12(9), 819–823 (2002)

    Article  Google Scholar 

  16. Linde, Y., Buzo, A., Gary, R.M.: An Algorithm for Vector Quantization Design. IEEE Transactions on Commnunications 28(1), 84–95 (1980)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department Computer Science and Information Engineering, Asia University, No. 500, Lioufeng Rd., Wufeng, Taichung, 41354, Taiwan, R.O.C.

    Yung-Chen Chou, Shu-Huan Chen & Min-Rui Hou

Authors
  1. Yung-Chen Chou
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shu-Huan Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Min-Rui Hou
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yung-Chen Chou .

Editor information

Editors and Affiliations

  1. HIGH-TECH Industry Development Zone, Nanchang Institute of Technology, Jiangxi Province, China

    Hui Sun

  2. Department of Computer Science and Information Engineering, National Penghu University of Science and Technology, Magong City, Taiwan

    Chin-Yu Yang

  3. Innovative Information Industry Research Center, Shenzhen University Harbin Institute of Technology, Shenzhen, China

    Chun-Wei Lin

  4. Harbin Institute of Technology Innovative Info. Industry Res. Center, Shenzhen University, Shenzhen, China

    Jeng-Shyang Pan

  5. Department of Computer Science Faculty of Elec. Eng. & Comp. Sci., VSB-Technical University of Ostrava, Ostrava, Czech Republic

    Vaclav Snasel

  6. Scientific Network for Innovation and Research Excellence, Machine Intelligence Research Labs (MIR Labs), Auburn, Washington, USA

    Ajith Abraham

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Chou, YC., Chen, SH., Hou, MR. (2015). A Quality Improving Scheme for VQ Decompressed Image Based on DCT. In: Sun, H., Yang, CY., Lin, CW., Pan, JS., Snasel, V., Abraham, A. (eds) Genetic and Evolutionary Computing. Advances in Intelligent Systems and Computing, vol 329. Springer, Cham. https://doi.org/10.1007/978-3-319-12286-1_20

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-12286-1_20

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-12285-4

  • Online ISBN: 978-3-319-12286-1

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation