Comparison and Analysis of Discrete Cosine Transform based Joint Photographic Experts Group Image Compression using Robust Watermarking Algorithm
S.M. Ramesh & Dr. A. Shanmugam
American Journal of Applied Sciences, Volume 8, Issue 1, pp. 63–70 · doi.org/10.3844/ajassp.2011.63.70
Abstract
Problem statement: The performance of the watermarking algorithms is analyzed using different compression standards to study the watermark extraction behavior of these algorithms. Approach: A digital watermarking method is said to be effective if the watermark embedded in an image by it could survive against diverse attacks ranging from compression, filtering to cropping. Several techniques are presented in the literature for robust watermarking algorithm to defend against the various the compression methods. Results: In this study, we have presented comparison and analysis of recently developed watermarking algorithms. Then, an extensive analysis is carried out to estimate and compare robustness of watermarking algorithms by considering the visual quality of the original and watermarked images in terms of Peak Signal to Noise Ratio. Furthermore, the extracting fidelity of the watermarking algorithms is compared by taking the Normalized Correlation value between the original watermark and the extracted watermark. Conclusion/Recommendations: The experimental results showed the accuracy of different watermarking algorithms in terms of visual quality and fidelity.
Licence and reuse
Free to read. This article carries the publisher’s copyright, as printed on it: © 2011 Science Publications. It is free to read; ask us before reusing it.
How to reuse this articleResponses (0)
About this article
Ramesh, S. & Shanmugam, D. A. (2011). Comparison and Analysis of Discrete Cosine Transform based Joint Photographic Experts Group Image Compression using Robust Watermarking Algorithm. American Journal of Applied Sciences, 8(1), 63–70. https://doi.org/10.3844/ajassp.2011.63.70
- Licence
- © Science Publications
- Issue
- Volume 8, Issue 1
- Policies
- Editorial independenceUse of generative AIData availabilityAppeals and complaints
- Version
- Version of record
