MULTI VARIATE NEURO-STATISTICAL SPARSE TRANSFORM FOR GRAY SCALE IMAGES

Authors

DOI:

https://doi.org/10.52292/j.laar.2022.583

Keywords:

Grayscale images, Neuro Statistical, Compression, Binary space partition technique

Abstract

Abstract-- The main objective of this paper is to examine the performance of  Neuro- Statistical sparse transformation function for implementation in a still image vector coding based compression system. This paper discusses the important features of low bit-rate image coding which is based on recent developments in the theory of multivariate nonlinear piecewise polynomial approximation in still images. It combines Binary Space Partition (BSP) scheme with Geometric Wavelet (GW) tree approximation so as to efficiently capture curve singularities and provide a sparse representation of the image. The quality of the reconstructed image is measured objectively using Peak Signal to Noise Ratio. Experimental results show that the proposed image compression system yields higher compression with minimal loss.

Author Biographies

  • G.Muthulakshmi, Manonmaniam Sundaranar University

    Assistant Professor

    Department of Computer Science and Engineering

    Manonmaniam Sundaranar University

    Tirunelveli, India

  • T.Ganesh Kumar, Galgotias University

    Associate Professor

    School of Computing Science and Engineering

    Galgotias University

    NCR Delhi

  • G.BALASUBRAMANIAN, Government College of Engineering

    Assistant Professor

    Department of Electrical and Electronics Engineering

    Government College of Engineering

    Tamilnadu, India

  • Priti Rishi, SRM University

    Associate Professor

    Department of Electronics and Communication Engineering

    SRM University

    NCR Delhi

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Published

2022-03-25

Issue

Vol. 52 No. 2 (2022): Latin American Applied Research

Section

Control and Information Processing

License

Copyright (c) 2022 Latin American Applied Research - An international journal

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