Table of Contents
ISRN Signal Processing
Volume 2014, Article ID 838315, 18 pages
http://dx.doi.org/10.1155/2014/838315
Research Article

Statistically Matched Wavelet Based Texture Synthesis in a Compressive Sensing Framework

Department of Electrical Engineering, Indian Institute of Technology Delhi, Hauz Khas, New Delhi 110016, India

Received 11 November 2013; Accepted 29 December 2013; Published 17 February 2014

Academic Editors: C.-W. Kok, S. Kwong, A. M. Peinado, and A. Rubio Ayuso

Copyright © 2014 Mithilesh Kumar Jha et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

This paper proposes a statistically matched wavelet based textured image coding scheme for efficient representation of texture data in a compressive sensing (CS) frame work. Statistically matched wavelet based data representation causes most of the captured energy to be concentrated in the approximation subspace, while very little information remains in the detail subspace. We encode not the full-resolution statistically matched wavelet subband coefficients but only the approximation subband coefficients (LL) using standard image compression scheme like JPEG2000. The detail subband coefficients, that is, HL, LH, and HH, are jointly encoded in a compressive sensing framework. Compressive sensing technique has proved that it is possible to achieve a sampling rate lower than the Nyquist rate with acceptable reconstruction quality. The experimental results demonstrate that the proposed scheme can provide better PSNR and MOS with a similar compression ratio than the conventional DWT-based image compression schemes in a CS framework and other wavelet based texture synthesis schemes like HMT-3S.