Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Sub-band discrete cosine transform-based greyscale image watermarking using general regression neural network

Sub-band discrete cosine transform-based greyscale image watermarking using general regression... In this paper, a new grey scale image watermarking scheme based on sub-band discrete Cosine transform (SB-DCT) using general regression neural network (GRNN) is proposed. The image features are extracted by applying the SB-DCT to each non-overlapping block of the image. These features are used to form the dataset, which act as input to GRNN. The output obtained by GRNN is used to embed the binary watermark logo in the selected low variance blocks of the image. Owing to the good function approximation and high generalisation property of GRNN, we are able to recover the watermark after performing several image processing operations. Through the extensive experimental results, high peak signal-to-noise ratio (PSNR) value of watermarked image and high bit correct ratio (BCR), normalised correlation (NC) value of the extracted watermark proves the imperceptibility and robustness of the proposed scheme compared to the state-of-art techniques. Keywords: bit correct ratio; discrete cosine transform; general regression neural network; normalised correlation; sub-band decomposition. Reference to this paper should be made as follows: Mehta, R., Rajpal, N. And Vishwakarma, V.P. (2015) `Sub-band discrete cosine transform-based greyscale image watermarking using general regression neural network', Int. J. Signal and Imaging Systems Engineering, Vol. 8, No. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Signal and Imaging Systems Engineering Inderscience Publishers

Sub-band discrete cosine transform-based greyscale image watermarking using general regression neural network

Loading next page...
 
/lp/inderscience-publishers/sub-band-discrete-cosine-transform-based-greyscale-image-watermarking-Mh6Z0GEzBc

References

References for this paper are not available at this time. We will be adding them shortly, thank you for your patience.

Publisher
Inderscience Publishers
Copyright
Copyright © 2015 Inderscience Enterprises Ltd.
ISSN
1748-0698
eISSN
1748-0701
DOI
10.1504/IJSISE.2015.072927
Publisher site
See Article on Publisher Site

Abstract

In this paper, a new grey scale image watermarking scheme based on sub-band discrete Cosine transform (SB-DCT) using general regression neural network (GRNN) is proposed. The image features are extracted by applying the SB-DCT to each non-overlapping block of the image. These features are used to form the dataset, which act as input to GRNN. The output obtained by GRNN is used to embed the binary watermark logo in the selected low variance blocks of the image. Owing to the good function approximation and high generalisation property of GRNN, we are able to recover the watermark after performing several image processing operations. Through the extensive experimental results, high peak signal-to-noise ratio (PSNR) value of watermarked image and high bit correct ratio (BCR), normalised correlation (NC) value of the extracted watermark proves the imperceptibility and robustness of the proposed scheme compared to the state-of-art techniques. Keywords: bit correct ratio; discrete cosine transform; general regression neural network; normalised correlation; sub-band decomposition. Reference to this paper should be made as follows: Mehta, R., Rajpal, N. And Vishwakarma, V.P. (2015) `Sub-band discrete cosine transform-based greyscale image watermarking using general regression neural network', Int. J. Signal and Imaging Systems Engineering, Vol. 8, No.

Journal

International Journal of Signal and Imaging Systems EngineeringInderscience Publishers

Published: Jan 1, 2015

There are no references for this article.