臺灣博碩士論文加值系統

在我們的研究中，主要發展基於H.264/AVC視訊壓縮標準之資料隱藏演算法，其目標在於能夠有效降低因為嵌入資料所引起的失真、甚至達到無損的目的。因此本論文提出兩種新的基於H.264/AVC視訊壓縮標準之資料隱藏方法，而方法的設計則是基於適應性變動長度編碼法的特性來進行，在每一種方法又可分為兩個演算法，其差別於細節部份。在一些H.264/AVC的文獻上得知，若能將位元率失真最佳化程序納入考慮則可有效降低因嵌入資料後所造成的損益，因此我們也將其程序納入使用。第一種方法為有損型資料隱藏演算法，藉由利用單一4×4區塊裡的所有Trailing one的極性值與最後一個非零係數的關係來達到嵌入資料的目的，實驗結果顯示在嵌入資料後所造成的失真可在接受的範圍，同時藉由我們提出的防止失真漫延的機制可以有效縮減因嵌入資料後所造成的失真，在可藏量的表現比起之前的文獻也是相當可觀。而第二種方法則屬於失真最小化型的資料隱藏演算法，其特性採用了直方圖位移法的觀念，將單一巨方塊裡頭的所有4×4區塊裡的最後一個非零係數藉由統計、預測與位移的方式來進行嵌入資料。實驗結果顯示，雖然在可藏量上的表現不如第一種來的好，但比起現有的可逆型方法卻可以更有效減少失真。因為是修改區塊裡最後一個非零係數的緣故，嵌入資料後所造的損益可達到有效的減少。此外，本論文所提出的方法很簡單，其計算複雜度很小。我們將提出的演算法應用在臉部辨識與驗證機制上藉此來證明我們所提出的演算法之實用性。

In this thesis, two kinds of novel data hiding schemes based on H.264/AVC video compression standard are presented. In each scheme, two different algorithms with varying processing details are designed. The design concepts are based on the properties found from the symbols of context adaptive variable length coding in the H.264/AVC coding standard in order for the controllable distortion when hiding data into these symbols. The proposed schemes take rate distortion optimization into account in order to minimize the changes on both the reproduced video quality and the output bit-rate. The first kind of methods is lossy, which utilize the relationship between the polarity value of all T1s and last nonzero coefficient to embed data into each 4×4 integer discrete cosine transform block. The experimental results demonstrated that two algorithms can provide good efficiency on both the reproduced video quality and the output bit-rate which are near or less to the original ones, and the payload of hiding data is higher than most of other similar methods in literatures. The second kind of method lies in minimizing the rate-distortion cost. Based on histogram shifting method, the algorithm collects the last nonzero coefficient for each 4×4 block in a macro block to embed data by prediction and shifting operations. Experimental results demonstrate that it can get good efficiency than existing reversible literatures, although the payload is lower than the first kind of method. The degradation can be minimized effectively because the methods only modify the last nonzero coefficient which brings slightly energy for an 4×4 block. Moreover, the computational complexity of the proposed method is low since only simple arithmetic computations are needed. Finally, the proposed methods are tested for the application of face detection and authentication to demonstrate the practicability of the research.

Abstract i
摘要 iii
Acknowledgements v
List of Table viii
List of Figure ix
Chapter 1 Introduction 1
1.1 Background 1
1.2 Motivation and objective 3
1.3 Requirements 6
1.4 Organize of the thesis 7
Chapter 2 Related Works 8
2.1 Reversible methods 11
2.1.1 Error resilient coding by difference expansion 12
2.1.2 Error concealment by histogram shifting 13
2.1.3 Prediction error expansion by histogram shifting 15
2.1.4 DCT-based watermarking on consideration of distortion drift 17
2.2 Robust methods 19
2.2.1 Coefficient pair selection and sub-band switching 19
2.2.2 Distortion prevention by spatiotemporal analysis 24
2.2.3 Robust video watermarking based on human visual model 26
2.3 Semi-Fragile method 27
2.3.1 Changing the number of nonzero coefficients 27
2.3.2 Authentication of compressed video by block classification 29
2.4 Fragile methods 30
2.4.1 Modulation coefficient method without intra--frame distortion drift 30
2.4.2 Relationship of odd and even for embedding data 32
2.4.3 Changing the number of T1s 33
2.4.4 The G-LSB based method 34
2.5 Summary 36
Chapter 3 The Proposed lossy method 38
3.1 Overview 38
3.2 The sign polarity of trailing one for video data hiding 43
3.2.1 The restriction condition: Coefficient Energy Difference rule 43
3.2.2 Embedding procedure 46
3.2.3 Extracting procedure 49
3.2.4 Experimental results 50
3.3 The improved sign polarity of trailing one of method 66
3.3.1 Embedding procedure 66
3.3.2 Extracting procedure 70
3.3.4 Distortion prevention by two thresholds adjustment 71
3.3.3 Experimental results 71
Chapter 4 The rate distortion minimization method 88
4.1 Coefficient prediction based reversible video data hiding 89
4.1.1 Overview 89
4.1.2 Prediction error generation by prediction pattern 90
4.1.3 Embedding procedure 95
4.1.4 Extraction procedure 98
4.1.5 Experimental results 99
4.2 Lossless coefficient prediction based method 118
4.2.1 Overview 118
4.2.2 System architecture and procedure 120
4.2.3 Experimental results 122
Chapter 5 Data hiding application in H.264/AVC 128
5.1 The experimental results for face detection 131
5.2 The proposed authentication framework 141
5.2.1 The experimental results 143
Chapter 6 Conclusion and Future Research 146
References 149
延伸摘要 155
簡歷 158

[1]C. I. Podilchuk and E. J. Delp, “Digital watermarking: algorithms and applications,” IEEE Signal Processing Magazine, vol. 18, no. 4, pp. 33-46, 2001.
[2]X. Zhang and S. Wang, “Efficient steganographic embedding by exploiting modification direction,” IEEE Communications Letters, vol. 10, no. 11, pp. 781-783, 2006.
[3]C. F. Lee, Y. R. Wang and C. C. Chang, “A steganographic method with high embedding capacity by improving exploiting modification direction,” in Proc. Intelligent Information Hiding and Multimedia Signal Processing, pp. 497-500, 2007.
[4]W. C. Kuo, L. C. Wuu, C. N. Shyi and S. H. Kuo, “A data hiding scheme with high embedding capacity based on general improving exploiting modification direction method,” in Proc. Hybird Intelligent Systems, pp. 69-72, 2009.
[5]Y. Q. Shi, Z. Ni, D. Zou, C. Liang and G. Xuan, “Lossless data hiding: fundamentals, algorithms and applications,” in Proc. IEEE International Symposium Circuits and Systems, pp.33-36, 2004.
[6]J. Tian, “Reversible data embedding using a difference expansion,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 13, no. 8, pp.890-896, 2003.
[7]A. M. Alattar, “Reversible watermarking using the difference expansion of a generalized integer transform,” IEEE Transactions on Image Processing, vol. 13, no. 8, pp.1147-1156, 2004.
[8]D. M. Thodi and J. J. Rodriguez, “Expansion embedding techniques for reversible watermarking,” IEEE Transactions on Image Processing, vol.16, no. 3, pp.721-730, 2007.
[9]G. Z. CHuan, W. J. Lin and N. Hong, “A low complexity reversible data hiding method based on modulus function,” in Proc. IEEE International Conference Signal Processing, pp. 2221-2224, 2008.
[10]C. C. Lin, S. P. Yang and N. L. Hsueh, “Lossless data hiding based on difference expansion without a location map,” in Proc. IEEE Congress on Image and Signal Processing, pp.8-12, 2008.
[11]Z. Ni, Y. Q. Shi, N. Ansari and W. Su, “Reversible data hiding,” IEEE Transactions on Circuits and Systems Video Technology, vol. 16, no. 3, pp. 354-362, 2006.
[12]K. L. Chung, Y. H. Huang, W. N. Yang and Y. C. Hsu, “Capacity maximization for reversible data hiding based on dynamic programming approach,” Applied Mathematics and Computation, vol.208, no. 1, pp 284-292, 2009.
[13]W. C. Kuo, D. J. Jiang and Y. C. Huang, “A reversible data hiding based on block division,” in Proc. IEEE Congress on Image and Signal Processing, pp 365-369, 2008.
[14]K. S. Kim, M. J. Lee and Y. H. Suh, “Reversible data exploiting spatial correlation between sub-sampled image,” Pattern Recognition, vol. 42, no.11, pp. 3083-3096, 2009.
[15]Y. C. Lin and T. S. Li, “High capacity histogram shifting lossless data hiding,” in Proc. IPPR Conference on Computer Vision, Graphics, and Image processing, pp.48-54, 2009.
[16]M. U. Celik, G. Sharma, A. M. Tekalp, and E. Saber “Reversible Data Hiding,” Proceedings of IEEE International Conference on Image Processing IV, Vol. 2, pp.157-160, 2002.
[17]S. Ohyama, M. Niimi, K. Yamawaki and H. Noda, “Lossless data hiding using bit-depth embedding for JPEG2000 compressed bit-stream,” International Conference on Intelligent Information hiding and Multimedia Signal Processing, pp. 151-154, 2008.
[18]W. N. Lie, Tom C. I. Lin, D. C. Tsai, and G. S. Lin, “Error Resilient Coding Based on Reversible Data Embedding Technique for H.264/AVC Video,” Proc. of IEEE International Conference on Multimedia & Expo, ICME, July 2005.
[19]K. L Chung, Y. H. Huang, P. C. Chang, and H. Y. M. Liao, “Reversible Data Hiding Based Approach for Intra- Frame Error Concealment in H.264/AVC,” IEEE Trans. Circuits and Systems for Video Technology, Vol. 20, No. 11, pp. 1643-1647, November, 2010.
[20]X. Zeng, Z. Cehn, M. Chen, and Z. Xiong, “Reversible Video Watermarking Using Motion Estimation and Prediction Error Expansion,” Journal of information science and engineering, Vol. 27, pp. 465-479, 2011.
[21]X. Zeng, Z. Chen, and Z. Xiong, “Issues and Solution on Distortion Drift in Reversible Video Data Hiding,” Multimedia Tools. pp. 465-484, 2011.
[22]Y. C. Lin and J. H. Li, ”Data Hiding with Rate-Distortion Optimization on H.264AVC Video” , the 23th IPPR Conference on Computer Vision, Graphics, and Image Processing. CVGIP. 15-17, August, 2010.
[23]Y. C. Lin and J. H. Li, “Data Hiding in the CAVLC Symbols of H.264/AVC Videos,” National Symposium on Telecommunications (NST), 2010.
[24]L. Tian, N. Zheng, J. Xue and T. Xu, “A CAVLC-Based Blind Watermarking Method for H.264/AVC Compressed Video”, IEEE Asia-Pacific Services Computing Conference, pp. 1295-1299, December, 2008.
[25]Z. Shahid, M. Chaumont and W. Puech, “Considering the Reconstruction Loop for Data Hiding of Intra- and Inter- frames of H.264/AVC,” Signal, Image and Video Processing, April 2010.
[26]K. Liao, D. Ye, S. Lian, Z. Guo, and J. Wang, “Efficient Information Hiding in H.264/AVC Video Coding,” Springer Science Business Media, Wednesday, June 23, 2010.
[27]X. Ma, Z. Li, H. Tu, and B. Zhang, “A Data Hiding Algorithm for H.264/AVC Video Streams Without Intra--Frame Distortion Drift,” IEEE Trans. on Circuits and System for Video Technology, vol. 20, no. 10, October, 2010.
[28]C. C. Wang, Y. C. Hsu, “A Fragile Watermarking Scheme for H.264/AVC Video Authentication,” Optical Engineering, Vol. 49, February 2010.
[29]S. D. Lin, C. Y. Chuang, and M.J. Chen, “A Watermarking Scheme by Changing the Number of Nonzero Coefficients in H.264/AVC Encoder”, the 22th IPPR Conference on Computer Vision, Graphics, and Image Processing. CVGIP. August. 23-25, 2009.
[30]D. J. Wang, T. Y. Chen, T. H. (C. H.) Chen, C. H. Huang, and C. Y. Lee, “Apply Semi-Fragile Watermarking to Authentication of Compressed Video Data,” Journal of Computers, Vol. 17, No. 2, pp.51-57, July. 2006.
[31]S. H. Yen, M. C. Chang, and C. J. Wang, “A Robust Video Watermarking Scheme of H.264,” the Joint Conferences on Pervasive Computing, JCPC, pp. 155-160, 2009.
[32]U. Z. Wu, Y. J. Wang, and W. H. Hsu, “Robust Watermark Embedding/Detection Algorithm for H.264 Video,” Journal of Electronic Imaging, vol. 14, 2005.
[33]A. Mansouri, A. M. Aznaveh, F. T. Azar, and F. Kurugollu, “A Low Complexity Video Watermarking in H.264 Compressed Domain,” IEEE Trans. on Information Forensics and Security, vol. 5, no. 4, December, 2010.
[34]M. Noorkami, and R. M. Mersereau, “A Framework for Robust Watermarking of H.264-Encoded Video with Controllable Detection Performance,” IEEE Trans. on Information Forensics and Security, vol. 2 no. 1, pp. 14~23, 2007.
[35]C. C. Chang, C. C. Lin, C. S. Tseng, and W. L. Tai, “Reversible hiding in DCT-based compressed images,” Information Sciences, vol. 177, no. 13, pp. 2768-2786, 2007.
[36]X. Zeng, Z. Y. Chen, M. Chen, and Z. Xiong, ” Invertible Image Watermarking based on Zero Coefficient Index,” Journal of Computer research and Development, 47(7), 2010.
[37]P. Tsai, Y. C. Hu, and H. L. Yeh, “Reversible image hiding scheme using predictive coding and histogram shifting,” Signal Processing, vol. 89, pp. 1129-1143, 2009.
[38]A. P. Fabien, R. J. Anderson, and M. G. Kuhn, “Information Hiding – A Survey,” Proceedings of the IEEE Special Issue on Protection of Multimedia Content, vol. 87, no. 7, pp. 1062-1078, 1999.
[39]J. Huang and Y. Q. Shi, “Reliable Information Bit Hiding,” IEEE Trans. on Circuits and Systems for Video Technology, vol. 12, no. 10, pp. 916-920, 2002.
[40]A. R. Bedford, “Industrial application of HDTV,” IEEE Communications Magazine, vol. 29, pp 25-27, August, 1991.
[41]R. Citta, P. Fockens, G. Krishnamurthy, and P. Snopko, “HDTV adaptability to multiple media,” IEEE Trans. on Consumer Electronics, vol. 35, Issue 3, pp. 195-201, 1999.
[42]K. Akune, K. Mase, Y. Hirano, and S. Kajita, “Discussion Support System for Video-oriented Sports Meetings,” First International Conference on Innovative Computing, Information and Control, ICICIC, vol. 2, pp. 561-564, 2006.
[43]A. Jaimes, T. Nagamine, J. Liu, K. Omura, and N. Sebe, “Affective Meeting Video Analysis,” IEEE International Conference on Multimedia and Expo, ICME, pp. 1412-1415, 2005.
[44]ITU-T Rec. H.264/ISO/IEC 14496-10, “Advanced Video Coding,” Final Committee Draft, Document JVTG050, March, 2003.
[45]K. Sühring, H.264/AVC Reference Software Group [On-line]. Available: http://iphome.hhi.de/suehring/tml/, Joint Model 12.2 (JM12.2), Jan. 2009.
[46]Iain E. G. Richardson, “H.264 and MPEG-4 Video Compression,” John Willy & Sons Ltd, 2003.
[47]M. Nilsson, J. Nordberg, and I. Claesson, “Face Detection Using Local SMQT Features and Split up Snow Classifier,” IEEE International Conference on Acoustics, Speech, and signal processing, (ICASSP), vol. 2, pp. 589-592, 2007.
[48]M. Nilsson, Face Detection for Matlab [On-line]. Available: http://www.mathworks.com/matlabcentral/fileexchange/13701/, Face Detection Using Local SMQT Features and Split up Snow Classifier, January, 2007.