MULTI VARIATE NEURO-STATISTICAL SPARSE TRANSFORM FOR GRAY SCALE IMAGES

Authors

  • G.Muthulakshmi Manonmaniam Sundaranar University
  • T.Ganesh Kumar Galgotias University https://orcid.org/0000-0002-2712-712X
  • G.BALASUBRAMANIAN Government College of Engineering
  • Priti Rishi SRM University

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

Published

2022-03-25

Issue

Section

Control and Information Processing