Skip to main content
SpringerLink
Log in

A general codebook design method for vector quantization

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Vector quantization (VQ) is widely used in image processing applications, the primary focus of VQ is to determine a codebook to represent the original image well. In order to make a codebook perform better on both distortion and bit rate (BR), a general codebook (GCB) for VQ is proposed in this paper. Unlike common codebook (CCB) or private codebook (PCB), GCB is a new structure of codebook where the codewords can either come from CCB or by training the input image. By applying the codewords in CCB that perform well and updating inactive codewords, only the new generated codewords and flags of codewords to be replaced are transmitted along with index table (IT). Therefore,the BR can be significantly reduced while the performance of distortion can be efficiently improved. The experimental results demonstrate that our proposed GCB has a better performance than CCB and various kinds of PCB-based methods.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Ayoobkhan MUA, Chikkannan E, Ramakrishnan K (2017) Lossy Image Compression Based on Prediction Error and Vector Quantisation. EURASIP Journal on Image and Video Processing 5

  2. Chang CC, Lin PY (2007) Significance-preserving codebook using generic algorithm. In: Proceeding FSKD ‘07 proceedings of the fourth international conference on fuzzy systems and knowledge discovery, August 24 - 27, vol 03. IEEE Computer Society, Washington, DC, pp 660–664

  3. Chang CC, Tai WL, Lin CC (2006) A reversible data hiding scheme based on side match vector quantization. IEEE Trans Circuits Syst Video Technol 16(10):1301–1308

    Article  Google Scholar 

  4. Chen L, Liu JTZ (2013) A statistical image resolution enhancement approach for mixed-resolution video. Int J Electron 101(9):1205–1216

    Article  Google Scholar 

  5. Chen Q, Kotani K, Lee F, Ohmi T (2010) Feature facial image recognition using VQ histogram in the DCT domain. Proc. SPIE 7546, second international conference on digital image processing, 75460J. p 7. https://doi.org/10.1117/12.855647

  6. Chris D, He X (2004) K-means clustering via principal component analysis. In: Proceeding ICML ‘04 proceedings of the twenty-first international conference on machine learning, Banff, Alberta, Canada — July 04 - 08. ACM, New York, pp 29

  7. Chuang JC, Hu YC (2011) An adaptive image authentication scheme for vector quantization compressed image. J Vis Commun Image Represent 22(5):440–449

    Article  Google Scholar 

  8. Dao SD, Abhary K, Marian R (2017) An innovative framework for designing genetic algorithm structures. Expert Syst Appl 90:196–208

    Article  Google Scholar 

  9. Feng HM, Chen CY, Ye F (2007) Evolutionary fuzzy particle swarm optimization vector quantization learning scheme in image compression. Expert Syst Appl 32(1):213–222

    Article  Google Scholar 

  10. Fränti P (2000) Genetic algorithm with deterministic crossover for vector quantization. Pattern Recogn Lett 21(1):61–68

    Article  Google Scholar 

  11. Hammer B et al (2014) Learning vector quantization for (dis-)similarities. Neurocomputing 131(7):43–51

    Article  Google Scholar 

  12. Hu YC, Chen WL, Tsai PY (2015) Refined codebook for grayscale image coding based on vector quantization. Opt Eng 54(7):073110

    Article  Google Scholar 

  13. Hu KC, Chen CC, Tsai CW, Chiang MC (2015) An enhanced initialization method for codebook generation. In: Proc. IEEE International Conference on Consumer Electronics - Taiwan (ICCE-TW), 6-8 June. IEEE, Taipei, pp 92–93

  14. Jolliffe IT (1986) Principal component analysis for special types of data. Springer, New York, pp 199–222

    Google Scholar 

  15. Lakshmi M, Senthilkumar J, Suresh Y (2016) Visually lossless compression for Bayer color filter array using optimized vector quantization. Appl Soft Comput 46(C):1030–1042

    Article  Google Scholar 

  16. Li C, Liu JTZ (2013) A statistical image resolution enhancement approach for mixed-resolution video. Int J Electron 101(9):1205–1216

    Google Scholar 

  17. Linde Y, Buzo A, Gray RM (1980) An algorithm for vector quantizer design. IEEE Trans Commun 28(1):84–95

    Article  Google Scholar 

  18. Liu J, Lampinen J (2005) A fuzzy adaptive differential evolution algorithm. Soft Comput 9(6):448–462

    Article  MATH  Google Scholar 

  19. Lloyd S (1982) Least squares quantization in PCM. IEEE Trans Inf Theory 28(2):129–137

    Article  MathSciNet  MATH  Google Scholar 

  20. Lu T-C, Chang C-Y (2010) A survey of VQ codebook generation. J Inf Hiding Multimed Signal Process 1(3):190–203

    Google Scholar 

  21. Ma X, Pan Z, Hu S, Wang L (2014) Enhanced side match vector quantisation based on constructing complementary state codebook. IET Image Process 9(4):290–299

    Article  Google Scholar 

  22. Ma T, Wang Y, Tang M, Cao J, Tian Y, Al-Dhelaan A, Al-Rodhaan M (2016) LED: a fast overlapping communities detection algorithm based on structural clustering. Neurocomputing 207:488–500

    Article  Google Scholar 

  23. Mohamed Uvaze Ahamed A, Eswaran C and Kannan R (2016) Lossy image compression based on vector quantization using artificial bee Colony and genetic algorithms. International Conference on Computational Science and Technology, Kota Kinabalu

  24. Pan JS, McInnes FR, Jack MA (1995) VQ codebook design using genetic algorithms. Electron Lett 31(17):1418–1419

    Article  Google Scholar 

  25. Pan Z, Zhang Y, Kwong S (2015) Efficient motion and disparity estimation optimization for low complexity multiview video coding. IEEE Trans Broadcast 61(2):166–176

    Article  Google Scholar 

  26. Pan Z, Lei J, Zhang Y, Sun X, Kwong S (2016) Fast motion estimation based on content property for low-complexity H.265/HEVC encoder. IEEE Trans Broadcast 62(3):675–684

    Article  Google Scholar 

  27. Pan Z, Jin P, Lei J, Zhang Y, Sun X, Kwong S (2016) Fast reference frame selection based on content similarity for low complexity HEVC encoder. J Vis Commun Image Represent 40, Part B:516–524

    Article  Google Scholar 

  28. Park H, Park JI (2017) Rapid generation of the state codebook in side match vector quantization. IEICE Trans Inf Syst E100-D(8):1934–1937

    Article  Google Scholar 

  29. Peng Z, Wang G, Jiang H, Meng S (2017) Research and improvement of ECG compression algorithm based on EZW. Comput Methods Programs Biomed 145:157–166

    Article  Google Scholar 

  30. Song J, Choi J, Love DJ (2017) Common codebook millimeter wave beam design: designing beams for both sounding and communication with uniform planar arrays. IEEE Trans Commun 65(4):1859–1872

    Article  Google Scholar 

  31. Tsai P (2009) Histogram-based reversible data hiding for vector quantisation-compressed images. IET Image Process 3(2):100–114

    Article  MathSciNet  Google Scholar 

  32. Tsai JT, Chou PY, Chou JH (2015) Performance comparisons between PCA-EA-LBG and PCA-LBG-EA approaches in VQ codebook generation for image compression. Int J Electron 102(11):1–21

    Article  Google Scholar 

  33. Vesanto J (1999) SOM-based data visualization methods. Intelligent Data Analysis 3(2):111–126

    Article  MATH  Google Scholar 

  34. Wang Z, Xu X (2012) Improved SOM-based high-dimensional data visualization algorithm. Comput Inf Sci 5(4):112–115

    Google Scholar 

  35. Watanabe K (2015) Vector quantization based on ε-insensitive mixture models. Neurocomputing 165:32–37

    Article  Google Scholar 

  36. Zheng WM, Lu ZM, Burkhardt H (2006) Color image retrieval schemes using index histograms based on various spatial-domain vector quantizers. Int J Innov Comput Inf Control 2(6):1317–1326

    Google Scholar 

  37. Zhou Z, Wu QMJ, Huang F, Sun X (2017) Fast and accurate near-duplicate image elimination for visual sensor networks. Int J Distrib Sens Netw. https://doi.org/10.1177/1550147717694172

Download references

Acknowledgements

This work is supported in part by the Open Project Program of the State Key Lab of Novel Software Technology (Grant No. KFKT2016B14), Nanjing University, the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD), the Open Research Fund of Key Laboratory of Spectral Imaging Technology, Chinese Academy of Sciences (Grant No. LSIT201606D) and the Industrial Program of Zhejiang Province (Grant No. 2016C31G4180003).

Author information

Authors and Affiliations

  1. School of Electronic and Information Engineering, Xi’an Jiaotong University, No. 28, Xianning West Road, Xi’an, 710049, People’s Republic of China

    Rui Li, Zhibin Pan & Yang Wang

Authors
  1. Rui Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhibin Pan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yang Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zhibin Pan.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, R., Pan, Z. & Wang, Y. A general codebook design method for vector quantization. Multimed Tools Appl 77, 23803–23823 (2018). https://doi.org/10.1007/s11042-018-5700-7

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-018-5700-7

Keywords

Search

Navigation