Abstract
The paper presents bit-plane specific new measures to visualize the extensive statistical detail of an image. We compute the frequency of ones, maximum run length and correlation among rows (columns) in each bit-plane of an image. The computed measures give row-wise and column-wise structural detail at bit-plane level and help an interpreter to analyze given image deeply for its effective interpretation and understanding. In this paper, the application of these measures is shown in cryptography to statistically analyze the image ciphers. The simulation study shows that the proposed measures are very useful and can be applied in various image processing applications for pattern recognition and understanding of visual objects.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Eskicioglu, A.M., Fisher, P.S.: Image quality measures and their performance. IEEE Trans. Commun. 43(12), 2959–2965 (1995)
Sheikh, H.R., Bovik, A.C.: Image information and visual quality. IEEE Trans. Image Process. 15(2), 430–444 (2006)
Janssen, T.J.W.M., Blommaert, F.J.: Computational approach to image quality. Displays 21(4), 129–142 (2000)
Avcibas, I., Sankur, B., Sayood, K.: Statistical evaluation of image quality measures. J. Electron. Imag. 11(2), 206–223 (2002)
Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004)
Lin, W., Kuo, C.C.: Perceptual visual quality metrics: A survey. J. Vis. Commun. and Image Representation 22(4), 297–312 (2011)
Bouyer, P., et al.: Quantitative analysis of real-time systems using priced timed automata. J. Commun. ACM 54(9), 78–87 (2011)
George, A.G., Prabavathy, A.K.: A survey on different approaches used in image quality assessment. J. Emerging Technol. and Advanced Eng. 3(2), 197–203 (2013)
Keelan, B.W.: Handbook of Image Quality: Characterization and Prediction. Marcel Dekker Inc., New York (2002)
Streijl, R.C., Winkler, S., Hands, D.S.: Mean opinion score (MOS) revisited: methods and applications, limitations and alternatives. Multimedia Syst. 22(2), 213–227 (2016)
Lahouhou, A., Viennet, E., Beghdadi, A.: Selecting low-level features for image quality assessment by statistical methods. J. Comput. Inf. Technol. CIT 18(2), 183–189 (2010)
Huber, P., Ronchetti, E.: Robust Statistics. Wiley, New York (2009)
Doane, D.P., Seward, L.E.: Measuring skewness: a forgotten statistics. J. Stat. Educ. 19(2), 1–18 (2011)
Neto, A.M., Victorino, A.C., Fantoni, I., Zampieri, D.E.: Automatic regions-of-interest selection based on Pearsons correlation coefficient. In: IEEE International Conference on Intelligent Robots and Systems, California, U.S., pp. 33–38 (2011)
Usama, M., Khan, M.K., Alghathbar, K., Lee, C.: Chaotic-based secure satellite imagery cryptosystem. Comput. Math. Appl. 60(2), 326–337 (2010)
Flusser, J., Suk, T.: Pattern recognition by affine moment invariants. Pattern Recogn. 26(1), 167–174 (1993)
Alaa, E., Hasan, D.: Co-occurrence matrix and its statistical features as a new approach for face recognition. Turk J. Elec. Eng. Comp. Sci. 19(1), 97–107 (2011)
Haddon, J.F., Boyce, J.F.: Co-occurrence matrices for image analysis. IEEE Electron. Commun. Eng. J. 5(2), 71–83 (1993)
Zhou, W., Bovik, A.C.: Mean Squared Error: love it or leave it? A new look at Signal Fidelity Measures. IEEE Sig. Processing Mag. 26(1), 98–117 (2009)
Chai, T., Draxler, R.R.: Root mean square error (RMSE) or mean absolute error (MAE)? Arguments against avoiding RMSE in the literature. Geoscientif Model Dev. 7(1), 1247–1250 (2014)
Zhang, N., Vladar, A.E., Postek, M.T., Larrabee, B.: A kurtosis-based statistical measure for two-dimensional processes and its application to image sharpness. Proc. Sect. Phys. Eng. Sci. Am. Stat. Soc., 4730–4736 (2003)
Ratan, R.: Securing images using inversion and shifting. In: Deep, K., Nagar, A., Pant, M., Bansal, J. (eds.) SocProS 2011. AISC, vol. 131, pp. 401–412. Springer, New Delhi (2012). https://doi.org/10.1007/978-81-322-0491-6_38
Katzenbeisser, S., Petitcolas, F.A.P.: Information Hiding Techniques for Steganography and Digital Watermarking. Artech House, Norwood (2000)
Jain, A.K.: Fundamentals of Digital Image Processing. Prentice Hall, Englewood Cliffs (1995)
Menezes, A.J., Vanstone, S.A., Van Oorschot, P.C.: Handbook of Applied Cryptography. CRC Press, Boca Raton (1996)
El-Samie Fathi, E.A., et al.: Image Encryption: A Communication Perspective. CRC Press, Boca Raton (2017)
Stinson, D.R.: Cryptography: Theory and Practice. Discrete Mathematics and Its Applications. Chapman & Hall/CRC Press, Ontario (2005)
Zhang, W., Wong, K., Hai, Y., Zhu, Z.: An image encryption scheme using lightweight bit-level confusion and cascade cross circular diffusion. Optics Commun. 285(9), 2343–2354 (2012)
Fu, C. and Zhu, Z.: A chaotic encryption scheme based on circular bit shift method. In: International Conference for Young Computer Scientists, pp. 3057–3061. IEEE Computer Society, Los Alamitos (2008)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Ratan, R., Arvind (2019). Bit-Plane Specific Measures and Its Applications in Analysis of Image Ciphers. In: Thampi, S., Marques, O., Krishnan, S., Li, KC., Ciuonzo, D., Kolekar, M. (eds) Advances in Signal Processing and Intelligent Recognition Systems. SIRS 2018. Communications in Computer and Information Science, vol 968. Springer, Singapore. https://doi.org/10.1007/978-981-13-5758-9_24
Download citation
DOI: https://doi.org/10.1007/978-981-13-5758-9_24
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-5757-2
Online ISBN: 978-981-13-5758-9
eBook Packages: Computer ScienceComputer Science (R0)