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Some Studies on Quality Metrics for Information Hiding


Affiliations
1 RCC Institute of Information Technology, Kolkata 700015, West Bengal, India
2 Guru Nanak Institute of Technology, Kolkata-700114, West Bengal, India
 

Information hiding is popular technique to deal with copyright fraud and uncontrollable distribution of multimedia content but developers and researchers yet to get a standardized way regarding performance evaluation of information hiding schemes. This paper deals with the essential quality metrics which are used to measure and monitor the impairments of data caused by information hiding. Preferably, quality metrics should have the skill to emphasize the advantages and the weaknesses of the hiding method under test and allow for easy and efficient method. It is helpful for researchers in order to accurately predict the results and to score in new algorithm development as well as to compare different information hiding algorithms altogether on a perceptual quality viewpoint.

Keywords

Copyright, Impairments, Information Hiding, Performance Evaluation, Perceptual Quality, Quality Metrics.
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  • Hartung F, Ramme F. Digital rights management and watermarking of multimedia content form commerce applications. IEEE Communications Magazine. 2000 Nov; 38(11):78–84.

  • Anderson E. Information Hiding. Proceedings of the first Workshop on Information Hiding, LNCS-1174; New York: Springer Verlag; 1996.

  • Katzenbesser S, Petitcoals FAP. Information hiding techniques for steganography and digital watermarking. Boston, MA: Artech House; 2000.

  • Gonzalez RC, Woods RE. Digital image processing. 3rd Ed. Pearson Prentice Hall; 2008.

  • Pappas TN, Safranek RJ. Perceptual criteria for image quality evaluation. Handbook of Image and Video Processing. New York: A. C Bovik Ed; Academic; May 2000. Special issue on image and video quality metrics. Signal Process. 1998 Nov; 70.

  • Hmood AK, Kasirun ZM, Jalab HA, Alam GM, Zaidan AA, Zaidan BB. On the accuracy of hiding information metrics: Counterfeit protection for education and important certificates. Int Journal of the Physical Sciences. 2010 Aug; 5(7):1054–62.

  • Liu Z, Karam LJ, Watson AB. JPEG2000 Encoding with perceptual distortion control. IEEE Tran Image Processing. 2006 Jul; 15(7):1763–78.

  • Wang Z, Bovik AC, Sheikh HR, Simoncelli EP. Image quality assessment: From error visibility to structural similarity. IEEE Tran Image Processing. 2004 Apr; 13(4):600–12.

  • Sheikh HR, Bovik AC.,Image Information and Visual Quality. IEEE Tran Image Processing. 2006 Feb; 15(2):430–44.

  • Ali AH. Combined DWT - DCT digital image watermarking. J Comput Sci. 2007; 3:740–6.

  • Lu CS. Multimedia security: Steganography and digital watermarking techniques for protection of intellectual property. 1st Ed. Taiwan, ROC: Idea Group Publishing; 2005. p. 350.ISBN: 10: 1591401925.

  • Luo M, Bors AG. Shape watermarking based on minimizing the quadratic error metric. IEEE International Conference on Shape Modeling and Applications (SMI); Beijing. 2009 Jun 26-28. p. 103–10.

  • Wang S, Zheng D, Zhao J, Tam WJ, Speranza F. An image quality evaluation method based on digital watermarking. IEEE Transactions on Circuits and Systems for Video Technology. 2007 Jan; 17(1):98–105.

  • Corsini M, Gelasca ED, Ebrahimi T, Barni M. Watermarked 3-D Mesh Quality Assessment. IEEE Transactions on Multimedia.2007 Feb; 9(2):247–56.

  • Zhang F, Liu W, Lin W, Ngan KN. Spread spectrum image watermarking based on perceptual quality metric. IEEE Transactions on Image Processing. 2011 Nov; 20(11):3207–18.

  • Avcibas I, Memon N, Sankur B. Steganalysis using image quality metrics. IEEE Transactions on Image Processing. 2003 Feb; 12(2):221–9.

  • Wang S, Zheng D, Zhao J, Tam WJ, Speranza F. Adaptive watermarking and tree structure based image quality estimation. IEEE Transactions on Multimedia. 2004 Feb; 16(2):311–25.

  • Raúl RC, Claudia FU, Trinidad-Blas Gershom de J. Data hiding scheme for medical images. 17th International Conference on Electronics, Communications and Computers (CONIELECOMP’07); 2007 Feb 26-28. p. 32.

  • Fan K, Pei Q, Mo W, Zhao X, Li X. A test platform for the security and quality of video watermarking content protection system. International Conference on Communication Technology ICCT 06; Guilin. 2006 Nov 27-30. p. 1–4.

  • Benedetto F, Giunta G, Neri A. QoS assessment of 3G video-phone calls by tracing watermarking exploiting the new colour space ‘YST’. IET Communications. 2007 Aug; 1(4):696–704.

  • Lavoue G, Corsini M. A comparison of perceptually-based metrics for objective evaluation of geometry processing. IEEE Transactions on Multimedia. 2010 Nov; 12(7):636–49.

  • Thakur MK, Saxena V, Gupta JP. A performance analysis of objective video quality metrics for digital video watermarking. 3rd IEEE International Conference on Computer Science and Information Technology (ICCSIT); Chengdu. 2010 Jul 9-11. p. 12–7.

  • Pankajakshan V, Autrusseau F. A multi-purpose objective quality metric for image watermarking. 17th IEEE International Conference on Image Processing; 2010 Sep 26-29. p. 2589–92.

  • Gunjal BL, Manthalkar RR. Discrete wavelet transform based strongly robust watermarking scheme for information hiding in digital images. Third International Conference on Emerging Trends in Engineering and Technology; Goa. 2010 Nov 19-21. p. 124–9.

  • Tsai MJ, Liu J. The quality evaluation of image recovery attack for visible watermarking algorithms. IEEE Conference on Visual Communications and Image Processing; Tainan. 2011 Nov 6-9. p. 1–4.

  • Kutter M, Petitcolas FAP. A fair benchmark for image watermarking systems. IEEE International Conference on Multimedia Computing and Systems. 1999; 3657:226–39.

  • Miyahara M, Kotani K, Algazi VR. Objective Picture Quality Scale (PQS) for image coding. IEEE Transactions on Communications. 1998 Sep; 46(9):1215–26.

  • Glover I, Grant P. Digital Communications. Prentice Hall; 1998.

  • Kullback S, Leibler RA. On information and sufficiency. Annals of Mathematical Statistics. 1951; 22(1):79-86.

  • Wang Z, Bovik AC. A universal image quality index. IEEE Signal Processing Letters. 2002 Mar; 9(3):81–4.

  • Wang Z, Bovik AC, Sheikh HR, Simoncelli EP. Image quality assessment: From error measurement to structural similarity. IEEE Tran Image Process. 2004 Apr; 13(4):600–12.

  • Chandler DM, Hemami SS. VSNR: A wavelet-based Visual Signal-toNoise Ratio for Natural Images. IEEE Transactions on Image Processing. 2007 Sep; 16(9):2284–98.

  • Sheikh HR, Bovik AC. Image information and visual quality. IEEE Tran Image Processing. 2006 Feb; 15(2):430–44.

  • Lin Y, Abdulla WH. Perceptual evaluation of audio watermarking using objective quality measures. ICASSP; Laa Vegas, NV. 2008. p. 1745–8.

  • Bhuiyan MIH. Rahman, R. DCT - Domain Watermark Detector using a normal inverse Gaussian prior. IEEE CCECE; Calgary, AB. 2010 May 2-5. p. 1–4.

  • Avcibas. Image quality statistics and their use in steganalysis and compression. [PhD Thesis]. Institute for Graduate Studies in Science and Engineering, Uludag University; 2001.


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  • Some Studies on Quality Metrics for Information Hiding

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Authors

Abhishek Basu
RCC Institute of Information Technology, Kolkata 700015, West Bengal, India
Anup Kolya
RCC Institute of Information Technology, Kolkata 700015, West Bengal, India
Partha Pratim Chowdhury
RCC Institute of Information Technology, Kolkata 700015, West Bengal, India
Angana Malik
RCC Institute of Information Technology, Kolkata 700015, West Bengal, India
Sritama Das
RCC Institute of Information Technology, Kolkata 700015, West Bengal, India
Samaresh Gayen
RCC Institute of Information Technology, Kolkata 700015, West Bengal, India
Ankur Mondal
Guru Nanak Institute of Technology, Kolkata-700114, West Bengal, India

Abstract


Information hiding is popular technique to deal with copyright fraud and uncontrollable distribution of multimedia content but developers and researchers yet to get a standardized way regarding performance evaluation of information hiding schemes. This paper deals with the essential quality metrics which are used to measure and monitor the impairments of data caused by information hiding. Preferably, quality metrics should have the skill to emphasize the advantages and the weaknesses of the hiding method under test and allow for easy and efficient method. It is helpful for researchers in order to accurately predict the results and to score in new algorithm development as well as to compare different information hiding algorithms altogether on a perceptual quality viewpoint.

Keywords


Copyright, Impairments, Information Hiding, Performance Evaluation, Perceptual Quality, Quality Metrics.

References