Home   >   CSC-OpenAccess Library   >    Manuscript Information
An Experimental Study into Objective Quality Assessment of Watermarked Images
Anurag Mishra, Aruna Jain, Manish Narwaria, Charu Agarwal
Pages - 199 - 219     |    Revised - 01-05-2011     |    Published - 31-05-2011
Volume - 5   Issue - 2    |    Publication Date - May / June 2011  Table of Contents
Digital Image Watermarking , Image Quality Assessment, PSNR, M-SVD, SSIM, Image Quality Score
In this paper, we study the quality assessment of watermarked and attacked images using extensive experiments and related analysis. The process of watermarking usually leads to loss of visual quality and therefore it is crucial to estimate the extent of quality degradation and its perceived impact. To this end, we have analyzed the performance of 4 image quality assessment (IQA) metrics – Structural Similarity Index (SSIM), Singular Value Decomposition Metric (M-SVD) and Image Quality Score (IQS) and PSNR on watermarked and attacked images. The watermarked images are obtained by using three different schemes viz., (1) DCT based random number sequence watermarking, (2) DWT based random number sequence watermarking and (3) RBF Neural Network based watermarking. The signed images are attacked by using five different image processing operations. We observe that the metrics behave identically in case of all the three watermarking schemes. An important conclusion of our study is that PSNR is not a suitable metric for IQA as it does not correlate well with the human visual system’s (HVS) perception. It is also found that the M-SVD scatters significantly after embedding the watermark and after attacks as compared to SSIM and IQS. Therefore, it is a less effective quality assessment metric for watermarked and attacked images. In contrast to PSNR and M-SVD, SSIM and IQS exhibit more stable and consistent performance. Their comparison further reveals that except for the case of counterclockwise rotation, IQS relatively scatters less for all other four attacks used in this work. It is concluded that IQS is comparatively more suitable for quality assessment of signed and attacked images.
CITED BY (5)  
1 Smitha, S. P. An Assessment Of Video Quality Using Watermark.
2 Wang, S., Zheng, D., Zhao, J., Tam, W. J., & Speranza, F. (2014). Adaptive watermarking and tree structure based image quality estimation. Multimedia, IEEE Transactions on, 16(2), 311-325.
3 Binbing, L. I. U., Ming, Z. H. A. O., & Haiqing, C. H. E. N. Quality assessment method for geometrically distorted images. Frontiers of Optoelectronics, 6(3), 275.
4 Liu, B., Zhao, M., & Chen, H. (2013). Quality assessment method for geometrically distorted images. Frontiers of Optoelectronics, 6(3), 275-281.
5 Wang, S. (2013). Digital Watermarking Based Image and Video Quality Evaluation (Doctoral dissertation, University of Ottawa).
1 Google Scholar 
2 CiteSeerX 
3 refSeek 
4 iSEEK 
5 Scribd 
6 SlideShare 
7 PdfSR 
A Shnayderman and A M. Eskicioglu, “Evaluating the Visual Quality of Watermarked Images”, IS&T/SPIE’s 18th Annual Symposium on Electronic Imaging, Security, Steganography, and Watermarking of Multimedia Contents VIII Conference, San Jose, CA, Jan. 15–19, 2006
A Shnayderman, A Gusev and A M. Eskicioglu, “An SVD-Based Gray-Scale Image Quality Measure for Local and Global Assessment”, IEEE Transactions on Image Processing, 15(2), February 2006
Bassem Abdel-Aziz and Jean Yves Chouinard, “On Perceptual Quality of Watermarked Images – An Experimental Approach”, In Proc. 2nd International Workshop on Digital Watermarking (IWDW 2003), Seoul, Korea, pp. 277-288, Oct. 2003
Charu Agarwal and Anurag Mishra, “A Novel Image Watermarking Technique using Fuzzy- BP Network”, In the Proc. Sixth International Conference on Intelligent Information Hiding and Multimedia Signal Processing, Darmstadt, Germany, pp.102-105, Oct. 15-17, 2010
Cheng-Ri Piao, Seunghwa Beack, Dong-Min Woo, and Seung-Soo Han, “A Blind Watermarking Algorithm Based on HVS and RBF Neural Network for Digital Image”, In Proc. International Conference on Neural Computing (ICNC ‘06), Part I, LNCS 4221, pp. 493-496, 2006
Der-Chyuan Lou, Ming-Chiang Hu, and Jiang-Lung Liu, “Healthcare Image Watermarking Scheme Based on Human Visual Model and Back-Propagation Network”, Journal of C.C.I.T, 37(1): 151-162, 2008
Hsiang-Cheh Huang, Yueh-Hong Chen and Ajith Abraham, “Optimized Watermarking Using Swarm-Based Bacterial Foraging”, Journal of Information Hiding and Multimedia Signal processing, 1(1):51-58, 2010
Ingemar J. Cox, Joe Kilian, F. Thomson Leighton, and Talal Shamoon, “Secure Spread Spectrum Watermarking for Multimedia”, IEEE Transactions on Image Processing, 6(12):1673-1687,1997
Manish Narwaria, Weisi Lin, “Scalable Image Quality Assessment Based on Structural Vectors”, In Proc. IEEE International Conference on Multimedia Signal Processing MMSP ’09, Rio de Janeiro, Brazil, Oct. 5-7, 2009
Mukesh C. Motwani and Fredrick C Harris, Jr, “Fuzzy Perceptual Watermarking for Ownership Verification”, in the Proc. 2009 International Conference on Image Processing, Computer Vision, and Pattern Recognition (IPCV'09), Las Vegas, Nevada, 2009
Rajesh Mehta, Anurag Mishra, Rampal Singh and Navin Rajpal, “Digital Image Watermarking in DCT Domain Using Finite Newton Support Vector Regression”, In the Proceedings of Sixth International Conference on Intelligent Information Hiding and Multimedia Signal Processing, Darmstadt, Germany, pp. 123 – 126, Oct. 15-17, 2010
Saraju P Mohanty, K R Ramakrishnan and Mohan Kankanhalli, “A Dual Watermarking Technique for Images”, ACM Multimedia, Part2, pp. 49-51, 1999
Shieh, C., H. Huang, F. Wang and J. Pan, “Genetic Watermarking based on Transform Domain Techniques”, Pattern Recognition Letters, vol. 37, pp. 555-565, 2004
V V. Pankajakshan and F. Autrusseau, “A Multi-Purpose Objective Quality Metric for Image Watermarking”, in Proc. IEEE International Conference on Image Processing (ICIP 2010), pp. 2589-2592, Hong Kong, Sept. 26-29, 2010
Zhou Wang, Alan C. Bovik and Hamid R. Sheikh, “Image Quality Assessment: From Error Measurement to Structural Similarity”, IEEE Transactions on Image Processing, 13(1), January 2004
Dr. Anurag Mishra
Deendayal Upadhyay College, University of Delhi - India
Mr. Aruna Jain
Bharti College, University of Delhi - India
Mr. Manish Narwaria
- Singapore
Mr. Charu Agarwal
- India

View all special issues >>