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DCT-Based Image Feature Extraction and Its Application in Image Self-Recovery and Image Watermarking

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DCT-Based Image Feature Extraction and Its Application in Image Self-Recovery and Image Watermarking

Hamid, Mohamed (2016) DCT-Based Image Feature Extraction and Its Application in Image Self-Recovery and Image Watermarking. Masters thesis, Concordia University.

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Abstract

Feature extraction is a critical element in the design of image self-recovery and watermarking algorithms and its quality can have a big influence on the performance of these processes. The objective of the work presented in this thesis is to develop an effective methodology for feature extraction in the discrete cosine transform (DCT) domain and apply it in the design of adaptive image self-recovery and image watermarking algorithms.
The methodology is to use the most significant DCT coefficients that can be at any frequency range to detect and to classify gray level patterns. In this way, gray level variations with a wider range of spatial frequencies can be looked into without increasing computational complexity and the methodology is able to distinguish gray level patterns rather than the orientations of simple edges only as in many existing DCT-based methods.
The proposed image self-recovery algorithm uses the developed feature extraction methodology to detect and classify blocks that contain significant gray level variations. According to the profile of each block, the critical frequency components representing the specific gray level pattern of the block are chosen for encoding. The code lengths are made variable depending on the importance of these components in defining the block’s features, which makes the encoding of critical frequency components more precise, while keeping the total length of the reference code short. The proposed image self-recovery algorithm has resulted in remarkably shorter reference codes that are only 1/5 to 3/5 of those produced by existing methods, and consequently a superior visual quality in the embedded images. As the shorter codes contain the critical image information, the proposed algorithm has also achieved above average reconstruction quality for various tampering rates.
The proposed image watermarking algorithm is computationally simple and designed for the blind extraction of the watermark. The principle of the algorithm is to embed the watermark in the locations where image data alterations are the least visible. To this end, the properties of the HVS are used to identify the gray level image features of such locations. The characteristics of the frequency components representing these features are identifying by applying the DCT-based feature extraction methodology developed in this thesis. The strength with which the watermark is embedded is made adaptive to the local gray level characteristics. Simulation results have shown that the proposed watermarking algorithm results in significantly higher visual quality in the watermarked images than that of the reported methods with a difference in PSNR of about 2.7 dB, while the embedded watermark is highly robustness against JPEG compression even at low quality factors and to some other common image processes.
The good performance of the proposed image self-recovery and watermarking algorithms is an indication of the effectiveness of the developed feature extraction methodology. This methodology can be applied in a wide range of applications and it is suitable for any process where the DCT data is available.

Divisions:Concordia University > Gina Cody School of Engineering and Computer Science > Electrical and Computer Engineering
Item Type:Thesis (Masters)
Authors:Hamid, Mohamed
Institution:Concordia University
Degree Name:M.A. Sc.
Program:Electrical and Computer Engineering
Date:15 August 2016
Thesis Supervisor(s):Wang, Chunyan
Keywords:Image Feature Extraction, Self-Recovery, Self-Embedding, Watermarking, Adaptive Encoding, Adaptive Embedding, HVS, DCT
ID Code:981491
Deposited By: Mohamed Hamid
Deposited On:08 Nov 2016 14:52
Last Modified:18 Jan 2018 17:53

References:

[1] T. Law, H. Itoh and H. Seki, "Image filtering, edge detection, and edge tracing using fuzzy reasoning," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 18, pp. 481-491, 1996.
[2] T. Ojala, M. Pietikainen and T. Maenpaa, "Multiresolution gray-scale and rotation invariant texture classification with local binary patterns," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, pp. 971-987, 2002.
[3] C. H. Lin, C. W. Liu and H. Y. Chen, "Image retrieval and classification using adaptive local binary patterns based on texture features," IET Image Processing, vol. 6, pp. 822-830, 2012.
[4] Z. Su, X. Luo, Z. Deng, Y. Liang and Z. Ji, "Edge-Preserving Texture Suppression Filter Based on Joint Filtering Schemes," IEEE Transactions on Multimedia, vol. 15, pp. 535-548, 2013.
[5] Hyun Sung Chang and Kyeongok Kang, "A compressed domain scheme for classifying block edge patterns," IEEE Transactions on Image Processing, vol. 14, pp. 145-151, 2005.
[6] J. Vega-Pineda, J. Rivera-Mejia, R. Sandoval-Rodriguez and G. Trujillo-Schiaffino, "Acceleration with FPGA for blocks and subblocks edge pattern classification in DCT domain images," in Proc. IEEE International Instrumentation and Measurement Technology Conference (I2MTC), May 2014, pp. 707-712.
[7] X. Wang, H. Wang and Y. Huang, "Fast Block Edge Direction Analysis in DCT domain," in Proc. International Conference on Wireless Communications and Signal Processing (WCSP), Oct. 2010, pp. 1-5.
[8] B. Shen and I.K. Sethi, "Direct feature extraction from compressed images," in Storage and Retrieval for Still Image and Video Databases IV, pp. 404-14, 1996.
[9] Hongliang Li, Guizhong Liu and Yongli Li, "An effective approach to edge classification from DCT domain," in Proc. International Conference on Image Processing, Sept. 2002, vol.1 pp. 940-943.
[10] M. Eom and Y. Choe, "Fast extraction of edge histogram in dct domain based on mpeg7," in Proc. of world academy of science, engineering and technology, 2005, pp. 209.
[11] J. Jiang, K. Qiu and G. Xiao, "A Block-Edge-Pattern-Based Content Descriptor in DCT Domain," IEEE Transactions on Circuits and Systems for Video Technology, vol. 18, pp. 994-998, 2008.
[12] D.K. Park, Y.S. Jeon and C.S. Won, "Efficient use of local edge histogram descriptor," in Proc. of the ACM workshops on Multimedia, Oct. 2000, pp. 51-54.
[13] I.J. Cox, J. Kilian, F.T. Leighton and T. Shamoon, "Secure spread spectrum watermarking for multimedia," IEEE Transactions On Image Processing, vol. 6, pp. 1673-1687, 1997.
[14] A.J. Ahumada and H.A. Peterson, "A visual detection model for DCT coefficient quantization," in Computing in Aerospace, pp. 314-318, 1993.
[15] H. Peterson, "DCT basis function visibility in RGB space," Society for Information Display Digest of Technical Papers, 1992.
[16] H.A. Peterson, H. Peng, J. Morgan and W.B. Pennebaker, "Quantization of color image components in the DCT domain," in Electronic Imaging'91, San Jose, CA, May 1991, pp. 210-222.
[17] A.B. Watson, J.A. Solomon, A.J. Ahumada Jr and A. Gale, "Discrete cosine transform (DCT) basis function visibility: effects of viewing distance and contrast masking," in IS&T/SPIE International Symposium on Electronic Imaging: Science and Technology, Feb. 1994, pp. 99-108.
[18] A.B. Watson, J.A. Solomon and A. Ahumada, "Visibility of DCT basis functions: Effects of display resolution," in Proc. Data Compression Conference (DCC'94), Mar. 1994, pp. 371-379.
[19] A.B. Watson, J. Solomon, A. Ahumada and A. Gale, "Visibility of DCT quantization noise: Effects of display resolution," in SID International Symosium Digest of Technical Papers, 1994, pp. 697-697.
[20] N. Jayant, J. Johnston and R. Safranek, "Signal compression based on models of human perception," Proceedings of the IEEE, 1993, vol. 81, pp. 1385-1422.
[21] A.B. Watson, "DCT quantization matrices visually optimized for individual images," in IS&T/SPIE's Symposium on Electronic Imaging: Science and Technology, Sep. 1993, pp. 202-216.
[22] Z. Wei and K. N. Ngan, "Spatio-Temporal Just Noticeable Distortion Profile for Grey Scale Image/Video in DCT Domain," IEEE Transactions on Circuits and Systems for Video Technology, vol. 19, pp. 337-346, 2009.
[23] J. Wu, G. Shi, W. Lin, A. Liu and F. Qi, "Just Noticeable Difference Estimation for Images With Free-Energy Principle," IEEE Transactions on Multimedia, vol. 15, pp. 1705-1710, 2013.
[24] S. H. Bae and M. Kim, "A Novel DCT-Based JND Model for Luminance Adaptation Effect in DCT Frequency," IEEE Signal Processing Letters, vol. 20, pp. 893-896, 2013.
[25] S. Bae and M. Kim, "A new DCT-based JND model of monochrome images for contrast masking effects with texture complexity and frequency," in Proc. IEEE International Conference of Image Processing, Sep. 2013, pp. 431-434.
[26] S. H. Bae and M. Kim, "A Novel Generalized DCT-Based JND Profile Based on an Elaborate CM-JND Model for Variable Block-Sized Transforms in Monochrome Images," IEEE Transactions on Image Processing, vol. 23, pp. 3227-3240, 2014.
[27] E. Esen and A. A. Alatan, "Robust Video Data Hiding Using Forbidden Zone Data Hiding and Selective Embedding," IEEE Transactions on Circuits and Systems for Video Technology, vol. 21, pp. 1130-1138, 2011.
[28] G. L. Wu, T. H. Wu and S. Y. Chien, "Algorithm and Architecture Design of Perception Engine for Video Coding Applications," IEEE Transactions on Multimedia, vol. 13, pp. 1181-1194, 2011.
[29] H. R. Wu, A. R. Reibman, W. Lin, F. Pereira and S. S. Hemami, "Perceptual Visual Signal Compression and Transmission," Proceedings of the IEEE, vol. 101, pp. 2025-2043, 2013.
[30] Z. Luo, L. Song, S. Zheng and N. Ling, "H.264/Advanced Video Control Perceptual Optimization Coding Based on JND-Directed Coefficient Suppression," IEEE Transactions on Circuits and Systems for Video Technology, vol. 23, pp. 935-948, 2013.
[31] W. Wan, J. Liu, J. Sun, X. Yang, X. Nie and F. Wang, "Logarithmic Spread-Transform Dither Modulation Watermarking Based on Perceptual Model," in Proc. IEEE International Conference on Image Processing, Sep. 2013, pp. 4522-4526.
[32] S. J. Park, G. Jeon and J. Jeong, "Deinterlacing algorithm using edge direction from analysis of the DCT coefficient distribution," IEEE Transactions on Consumer Electronics, vol. 55, pp. 1674-1681, 2009.
[33] J. Fridrich and M. Goljan, "Images with self-correcting capabilities," in Proc. International Conference on Image Processing, (ICIP 99), Oct. 1999, vol. 3, pp. 792-796.
[34] X. Zhang, S. Wang and G. Feng, "Fragile watermarking scheme with extensive content restoration capability," in International Workshop on Digital Watermarking, Aug. 2009, pp. 268-278.
[35] X. Zhang, Z. Qian, Y. Ren and G. Feng, "Watermarking With Flexible Self-Recovery Quality Based on Compressive Sensing and Compositive Reconstruction," IEEE Transactions on Information Forensics and Security, vol. 6, pp. 1223-1232, 2011.
[36] P. Korus and A. Dziech, "Efficient Method for Content Reconstruction With Self-Embedding," IEEE Transactions on Image Processing, vol. 22, pp. 1134-1147, 2013.
[37] S. Sarreshtedari and M. A. Akhaee, "A Source-Channel Coding Approach to Digital Image Protection and Self-Recovery," IEEE Transactions on Image Processing, vol. 24, pp. 2266-2277, 2015.
[38] P. Korus and A. Dziech, "Adaptive Self-Embedding Scheme With Controlled Reconstruction Performance," IEEE Transactions on Information Forensics and Security, vol. 9, pp. 169-181, 2014.
[39] Z. Qian and G. Feng, "Inpainting Assisted Self Recovery With Decreased Embedding Data," IEEE Signal Processing Letters, vol. 17, pp. 929-932, 2010.
[40] L. Zhao, Z. Qian, C. Qin and Y. Xie, "An image self-recovery approach with variable payload," in Proc. IEEE Conference on Industrial Electronics and Applications, Jun. 2014, pp. 1254-1257.
[41] M. El'arbi and C. Ben Amar, "Image authentication algorithm with recovery capabilities based on neural networks in the DCT domain," IET Image Processing, vol. 8, pp. 619-626, 2014.
[42] X. Zhang, S. Wang, Z. Qian and G. Feng, "Reference Sharing Mechanism for Watermark Self-Embedding," IEEE Transactions on Image Processing, vol. 20, pp. 485-495, 2011.
[43] R. Chamlawi, A. Khan and I. Usman, "Authentication and recovery of images using multiple watermarks," Computer and Electrical Eng., vol. 36, pp. 578-584, 2010.
[44] Y. Huo, H. He and F. Chen, "Alterable-capacity fragile watermarking scheme with restoration capability," Optical Communications, vol. 285, pp. 1759-1766, 2012.
[45] P. Korus and A. Dziech, "Reconfigurable self-embedding with high quality restoration under extensive tampering," in 2012 19th IEEE International Conference on Image Processing, Sep. 2012, pp. 2193-2196.
[46] Z. Qian, G. Feng, X. Zhang and S. Wang, "Image self-embedding with high-quality restoration capability," Digital Signal Processing, vol. 21, pp. 278-286, 2011.
[47] C. Qin, C. Chang and P. Chen, "Self-embedding fragile watermarking with restoration capability based on adaptive bit allocation mechanism," Signal Processing, vol. 92, pp. 1137-1150, 2012.
[48] C. Qin, C. Chang and K. Chen, "Adaptive self-recovery for tampered images based on VQ indexing and inpainting," Signal Processing, vol. 93, pp. 933-946, 2013.
[49] F. Bornemann and T. März, "Fast image inpainting based on coherence transport," Journal of Mathematical Imaging and Vision, vol. 28, pp. 259-278, 2007.
[50] N. Nikolaidis and I. Pitas, "Robust image watermarking in the spatial domain," Signal Processing, vol. 66, pp. 385-403, 5/28. 1998.
[51] D. M. Thodi and J. J. Rodriguez, "Expansion Embedding Techniques for Reversible Watermarking," IEEE Transactions on Image Processing, vol. 16, pp. 721-730, 2007.
[52] J. Tian, "Reversible data embedding using a difference expansion," IEEE Transactions on Circuits and Systems for Video Technology, vol. 13, pp. 890-896, 2003.
[53] B. Ou, X. Li, Y. Zhao, R. Ni and Y. Q. Shi, "Pairwise Prediction-Error Expansion for Efficient Reversible Data Hiding," IEEE Transactions on Image Processing, vol. 22, pp. 5010-5021, 2013.
[54] I. C. Dragoi and D. Coltuc, "Local-Prediction-Based Difference Expansion Reversible Watermarking," IEEE Transactions on Image Processing, vol. 23, pp. 1779-1790, 2014.
[55] G. Coatrieux, W. Pan, N. Cuppens-Boulahia, F. Cuppens and C. Roux, "Reversible watermarking based on invariant image classification and dynamic histogram shifting," IEEE Transactions on Information Forensics and Security, vol. 8, pp. 111-120, 2013.
[56] T. Zong, Y. Xiang, I. Natgunanathan, S. Guo, W. Zhou and G. Beliakov, "Robust Histogram Shape-Based Method for Image Watermarking," IEEE Transactions on Circuits and Systems for Video Technology, vol. 25, pp. 717-729, 2015.
[57] C.I. Podilchuk and Wenjun Zeng, "Image-adaptive watermarking using visual models," IEEE Journal On Selected Areas in Communications, vol. 16, pp. 525-539, 1998.
[58] Jiwu Huang, Y.Q. Shi and Yi Shi, "Embedding image watermarks in dc components," IEEE Transactions On Circuits and Systems for Video Technology, vol. 10, pp. 974-979, 2000.
[59] M. Cedillo-Hernandez, M. Nakano-Miyatake and H. Perez-Meana, "Robust watermarking to geometric distortion based on image normalization and texture classification," in Proc. Midwest Symposium on Circuits and Systems (MWSCAS), Aug. 2008, pp. 245-248.
[60] Xiuli Wang, Weihua Xie and Xuan Wang, "Adaptive Watermarking Algorithm of Image Based on the Human Visual System," in Proc. International Conference on Business Computing and Global Informatization (BCGIN), Dec. 2012, pp. 511-514.
[61] Shuang Zhi, Yana Zhang, Cheng Yang and Jianbo Liu, "Edge detection based JND model for digital watermarking," in Proc. International Conference on Signal Processing (ICSP), Oct. 2014, pp. 875-879.
[62] OuJun Lou, Li Shaohua, Liu ZhaoXia and Tang ShuangTong, "A Novel Multi-Bit Watermarking Algorithm Based on HVS," in Proc. International Symposium on Parallel Architectures, Algorithms and Programming (PAAP), Mar. 2014, pp. 278-281.
[63] Jung-San Lee and Bo Li, "Self-Recognized Image Protection Technique that Resists Large-Scale Cropping," IEEE MultiMedia, vol. 21, pp. 60-73, 2014.
[64] J. Varghese, O. B. Hussain, B. Babu, J. M. Basheer, S. Subash, M. R. Saadi and M. S. Khan, "An efficient DCT-SVD based algorithm for digital image watermarking," in Proc. International Carnahan Conference on Security Technology (ICCST), Oct. 2014, pp. 1-6.
[65] W. Wan, J. Liu, J. Sun, C. Ge and X. Nie, "Logarithmic STDM watermarking using visual saliency-based JND model," Electronics Letters, vol. 51, pp. 758-760, 2015.
[66] T. Zong, Y. Xiang, S. Guo and Y. Rong, "Rank-Based Image Watermarking Method With High Embedding Capacity and Robustness," IEEE Access, vol. 4, pp. 1689-1699, 2016.
[67] I. Nasir, F. Khelifi, J. Jiang and S. Ipson, "Robust image watermarking via geometrically invariant feature points and image normalisation," IET Image Processing, vol. 6, pp. 354-363, 2012.
[68] Xinge You, Liang Du, Yiu-ming Cheung and Qiuhui Chen, "A Blind Watermarking Scheme Using New Nontensor Product Wavelet Filter Banks," IEEE Transactions On Image Processing, vol. 19, pp. 3271-3284, 2010.
[69] K. Ramanjaneyulu and K. Rajarajeswari, "Wavelet-based oblivious image watermarking scheme using genetic algorithm," IET Image Processing, vol. 6, pp. 364-373, 2012.
[70] K. Zebbiche and F. Khelifi, "Efficient wavelet-based perceptual watermark masking for robust fingerprint image watermarking," IET Image Processing, vol. 8, pp. 23-32, 2014.
[71] N. B. Halima, M. A. Khan and R. Kumar, "A novel approach of digital image watermarking using HDWT-DCT," in Proc. Global Summit on Computer & Information Technology (GSCIT), Jun. 2015, pp. 1-6.
[72] B. Mathon, F. Cayre, P. Bas and B. Macq, "Optimal Transport for Secure Spread-Spectrum Watermarking of Still Images," IEEE Transactions on Image Processing, vol. 23, pp. 1694-1705, 2014.
[73] C. C. Lai and C. C. Tsai, "Digital Image Watermarking Using Discrete Wavelet Transform and Singular Value Decomposition," IEEE Transactions on Instrumentation and Measurement, vol. 59, pp. 3060-3063, 2010.
[74] J. Varghese, O. B. Hussain, B. Babu, J. M. Basheer, S. Subash, M. R. Saadi and M. S. Khan, "An efficient DCT-SVD based algorithm for digital image watermarking," in Proc. International Carnahan Conference on Security Technology (ICCST), Oct. 2014, pp. 1-6.
[75] M. Andalibi and D. M. Chandler, "Digital Image Watermarking via Adaptive Logo Texturization," IEEE Transactions on Image Processing, vol. 24, pp. 5060-5073, 2015.
[76] I.J. Cox, Digital watermarking and steganography, Burlington, MA: Elsevier/Morgan Kaufmann Publishers, 2008.
M. Hamid and C. Wang, "A simple image-adaptive watermarking algorithm with blind extraction," in Proc. International Conference on Systems, Signals and Image Processing (IWSSIP), May 2016.
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