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Compression of Gray Scale Images Using Linear Prediction on Wavelet Coefficients

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Advances in Computer Science and Information Technology. Computer Science and Information Technology (CCSIT 2012)

Abstract

Each year, terabytes of image data- both medical and non medical- are generated which substantiates the need of image compression. In this paper, the correlation properties of wavelets are utilised in linear predictive coding to compress images. The image is decomposed using a one dimensional wavelet transform. The highest level approximation and a few coefficients of details in every level are retained. Using linear prediction on these coefficients the image is reconstructed.With less predictors and samples from the original wavelet coefficients compression can be achieved. The results are appraised in objective and subjective manner.

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Authors and Affiliations

  1. Dept. of Electronics, Model Engineering College, Kochi, India

    P. S. Arya Devi & M. G. Mini

Authors
  1. P. S. Arya Devi
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  2. M. G. Mini
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Editor information

Editors and Affiliations

  1. Jackson State University, Jackson, MS, USA

    Natarajan Meghanathan

  2. University of Calcutta, Calcutta, India

    Nabendu Chaki

  3. Wireilla Net Solutions PTY Ltd., Melbourne, VIC, Australia

    Dhinaharan Nagamalai

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© 2012 ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering

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Arya Devi, P.S., Mini, M.G. (2012). Compression of Gray Scale Images Using Linear Prediction on Wavelet Coefficients. In: Meghanathan, N., Chaki, N., Nagamalai, D. (eds) Advances in Computer Science and Information Technology. Computer Science and Information Technology. CCSIT 2012. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 86. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27317-9_6

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  • DOI: https://doi.org/10.1007/978-3-642-27317-9_6

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-27316-2

  • Online ISBN: 978-3-642-27317-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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