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A Hybrid Approach of Wavelet Transform Using Lifting Scheme and Discrete Wavelet Transform Technique for Image Processing

  • K. Ramya laxmiEmail author
  • S. Pallavi
  • N. Ramya
Conference paper
Part of the Learning and Analytics in Intelligent Systems book series (LAIS, volume 3)

Abstract

Now a modern time many area such as company, medical, research and file require large number of image for general-purpose application to solve the complex problem. Image contain more information that require more storage space and transmission bandwidths, so the image compression is required to store only important information and reduce the different types of redundancy of image for storing and transmission in an efficient manner, because uncompressed image required more data storage capacity and transmission time. In the present work the storing space used is very less ecuase it help in reducing the processing time. For image compression, different transform technique is used. Image can be represented as a matrix of pixel values and after compression by applying different method there is no change or little change between pixel values. This present work is uses haar method and Lifting Wavelet Transform for image compression for increase the efficiency of Discrete Wavelet Transform (DWT).

Keywords

Discrete wavelet transforms (DWT), and lifting wavelet transform (LWT) Adaptive lifting wavelet transform 

References

  1. 1.
    Fang Z, Xiong N (2011) Interpolation-based direction-adaptive lifting DWT and modified SPIHT for image compression in multimedia communication. IEEE Syst J 5(4):584–593CrossRefGoogle Scholar
  2. 2.
    Grgic S, Grgic M, Zovko-Cihla B (2001) Performance analysis of image compression using wavelet. IEEE Trans Indust Electron 48(3):682–695CrossRefGoogle Scholar
  3. 3.
    Hilton ML, Jawerth BO, Sengupta A (1994) Compressing still and moving image with wavelet. Multimedia Syst 2(5):218–227CrossRefGoogle Scholar
  4. 4.
    AI-Kamali FS, Dessouky MI, Sallam BM, Shawki F, EI-Samie FEA (2010) Transceiver scheme for single-carrier frequency division multiple access implementing the wavelet transform and peak to –average-power ratio reduction method. IET Commun 4(1):69–79CrossRefGoogle Scholar
  5. 5.
    Taubman D, Marcellin MW (2002) JPEG 2000 image compression fundamentals standard and practice. Kluwer, Dordrecht, The NetherlandsCrossRefGoogle Scholar
  6. 6.
    Chen N, Wan W, Xiao HD (2010) Robust audio hashing based on discrete-wavelet-transform and nonnegative matrix factorisation. IET Commn 4(14):1722–1731CrossRefGoogle Scholar
  7. 7.
    Cands EJ, Donoho DL (1999) Curvelet a surprisingly effective nonadaptive representation for object with edges, in curve and surface fitting: Saint-malo. University Press, Nashville, TN, pp 105–120Google Scholar
  8. 8.
    Jangde K, Raja R (2013) Study of a image compression based on adaptive direction lifting wavelet transform technique. Int J Adv Innov Res (IJAIR) 2(8):ISSN: 2278 – 7844Google Scholar
  9. 9.
    Jangde K, Raja R (2014) Image compression based on discrete wavelet and lifting wavelet transform technique. Int J Sci, Eng Technol Res (IJSETR) 3(3):ISSN: 2278 – 7798Google Scholar
  10. 10.
    Rohit R, Sinha TS, Patra RK, Tiwari S (2018) Physiological trait based biometrical authentication of human-face using LGXP and ANN techniques. Int. J. of Inf Comput Secur 10(2/3):303–320 (Special Issue on: Multimedia Information Security Solutions on Social Networks)Google Scholar
  11. 11.
    Raja R, Mahmood MR, Patra RK (2018) Study and analysis of different pose invariant for face recognition under lighting condition. Sreyas Int J Sci Technocr 2(2):11–17Google Scholar
  12. 12.
    Raja R, Agrawal S (2017) An automated monitoring system for tourist/safari vehicles inside sanctuary. Indian J Sci Res 14(2):304–309, ISSN: 2250-0138Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of CSESreyas Institute of Engineering and TechnologyNagoleIndia

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