區塊特徵應用於影像視訊壓縮及浮水印之研究

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姓名

魯大德(Ta-Te Lu) 查詢紙本館藏

畢業系所

電機工程學系

論文名稱

區塊特徵應用於影像視訊壓縮及浮水印之研究
(Featured-based block-wise processing applied to image and video compression and watermarking systems)

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摘要(中)

小波轉換同時具有空間域(spatial)及頻域(frequency)的特性，適合做資料壓縮及浮水印嵌入。本文提出利用小波轉換不同次頻帶的數學統計特性，達到提昇影像/視訊品質與浮水印嵌入的目的。在小波域中分析不同次頻帶區塊的統計特性，高頻帶由於能量分佈不均，依據區塊特徴，我們發展出區塊重組(block reordering)機制將含能量多的區塊群聚達到高頻能量集中的目的，實驗結果顯示高頻區塊能量集中，在低位元率下能減少JPEG2000位元使用約6.88%及提昇視訊中運動補償殘餘值(motion compensation residual)的訊雜比約 0.69~1.28 dB。低頻帶由於能量分佈集中較能抗拒一般攻擊，利用區塊打亂重組後成員與均值間分佈距離狀況設定區塊極性，並且計算出每個區塊平滑度當成此區塊特徵，利用區塊平滑度中最小位元當成調變參數，藉由區塊極性及浮水印兩者關聯性來決定此區塊特徵調變量，達到嵌入浮水印的目的。除此之外我們利用小波轉換可調性(scalability)結合MPEG2處理超大視訊問題。小波轉換可調性可將超大視訊分解成不同方向性次頻帶(subband)，依不同次頻帶特徵設計不同量化表(quantization matrices)使編碼效果達到最佳。實驗結果顯示可以提昇單獨採用MPEG 2視訊品質的訊雜比0.3 ~ 1.5 dB。

摘要(英)

The characteristics of wavelet transform are suitable for data compression and image watermark embedding. In this dissertation, the mathematical statistics in wavelet domain are analyzed. Featured-based block-wise processing is applied to image/video compression and watermarking systems. The proposed method comprises four technologies: including the significant bit-plane clustering technique for image coding (SBPC), the selective block-wise reordering with SPIHT for video coding (SBR-SPIHT), a scalable coding method based on discrete wavelet transform-MPEG (DWT-MPEG) for video coding, and the block polarity and activity index modulation for watermark embedding.
The SBPC is applied to high-energy code blocks to enhance the energy compaction by rearranging the column positions in these code blocks. The energy compaction effect can improve the coding efficiency of JPEG2000, which results in an improvement of 6.88% bit-rate reduction at 0.1 bpp on average over JPEG2000.
The SBR-SPIHT enhances the video coding efficiency for motion-compensated residuals at very low bit-rates. The motion estimation and motion compensation schemes of H.263 are used to reduce the temporal redundancy. The residuals are then wavelet transformed. The block-mapping reorganization utilizes the wavelet zerotree relationship that jointly presents the wavelet coefficients from the lowest subband to high frequency subbands at the same spatial location, and allocates each wavelet tree with all descendents to form a wavelet block. The selective multi-layer block-wise reordering technique is then applied to those wavelet blocks that have energy higher than a threshold to enhance the energy compaction by rearranging the significant pixels in a block to the upper left corner based on the magnitude of energy. Simulation results demonstrate that SBR-SPIHT outperforms H.263 by 1.28~0.69 dB on average for various video sequences at very low bit-rates, ranging from 48 to 10 kbps.
The DWT-MPEG provides resolution-scalability such that low cost existing video codec can be used to support scalable video. In each subband, a fixed-size motion compensated MPEG coder with custom-designed quantization tables and scanning direction is employed. The simulation results show that the DWT-MPEG coding method improves the image quality over ordinary MPEG coding by 0.3 ~ 1.5 dB.
A blind watermarking method is implemented to embed watermark in wavelet domain. In this blind watermarking method, block polarity is first determined based on the number of coefficients that are larger than the median value. Then, the block activity index modulation is performed based on the XOR operation of the randomized watermark and the randomized wavelet blocks polarity. Activity index modulation is represented by the sum of absolute differences (SAD) of each coefficient to the median value. The proposed method not only survives the benchmark with non-geometric, image cropping, small degree rotation, and line removal attacks, but also achieves perceptual transparency, blind detection, and low false positive rate.

關鍵字(中)

★ 小波轉換
★ 區塊重組
★ 浮水印
★ 資料壓縮

關鍵字(英)

★ reordering
★ data compression
★ image watermarking
★ JPEG2000
★ SPIHT
★ bit-plane clustering

論文目次

1. Introduction ………………………………………………………………………………… 1
1.1 Motivation of the Research ……………………………………………………………… 1
1.2 Issues ……………………………………….…………………………………… 1
1.2.1 Feature-based block-wise processing for image/video data compression………… 2
1.2.2 Feature-based block processing for image watermark embedding ……………… 3
1.3 Contribution of the Research …………………………………………………………… 4
1.4 Organization of the Dissertation ………………………………………………………… 6
2. Overview of EBCOT and SPIHT Coding Algorithm ……………………………………… 9
2.1 JPEG 2000………………………………………... ……………………………………… 9
2.2 EBCOT…………....……………………………………………………………………… 10
2.3 SPIHT…………………………………………………………………………………… 14
3. Significant Bit-Plane Clustering Technique for JPEG 2000 Image Coding…... ………… 19
3.1 Introduction …………………………………………………………………………… 19
3.2 The Bit-plane Clustering Structure for JPEG 2000 …………………………...………… 21
3.3 Simulation Results ……………………………………………………………………… 27
4. Selective Blockwise Reordering SPIHT Video Coding…………………... ……………… 33
4.1 Introduction …………………………………………………………………………… 33
4.2 Selective Blockwise Reordering SPIHT Video Coding Algorithm …………………… 35
4.2.1 Intra-frame Coding …………………………………………………………… 35
4.2.2 Inter-frame Coding ……………………………………………………………… 36
4.3 Motion-Compensated Residuals Coding ………………………………………… 37
4.3.1 Block-mapping Reorganization………………………………………………… 38
4.3.2 Block Reordering…………………………………………………………………..… 39
4.3.3 Multi-layer Block Reordering………………………………………………… 44
4.3.4 Sorting Pass Modification of SPIHT Algorithm………………………………… 49
4.4 Simulation Results ……………………………………………………………………… 51
5. A Scalable Video Compressing Technique based on Wavelet Transform and MPEG Coding…………………………………………... ……………………………………………… 66
5.1 Introduction………………………… ……………………………………………………… 66
5.2 System Overview ……...…………………………………………………………………… 67
5.3 Modified MPEG Coding System…………………………………………………………… 69
5.3.1 Hierarchical Motion Estimation and Compensation………………… 70
5.3.2 DCT transform and quantization matrix design…………………………………… 71
5.3.3 Scanning directions and variable length coding…………………………………… 73
5.3.4 Optimal bit allocation…………………………...…………………………………… 74
5.4 Simulation Results…………………………………………………………… 76
6. Blind Image Watermarking based on Block Polarity and Activity Index Modulation……………………………………………………………………… 81
6.1 Introduction………………………… ……………………………………………………… 81
6.2 Watermark Embedding Structure…………………………………………………………… 83
6.2.1 Pseudo-random Permutation of Wavelet Blocks and Watermark………………… 84
6.2.2 Block Composition……………………………………………….………………… 85
6.2.3 Block Mapping…………………...………………………………..………………… 85
6.2.4 Watermark Embedding………………………………………………….…………… 86
6.2.5 Block Activity Index…………………………………………………….…………… 86
6.2.6 Inverse Permuatation………………………………………………….…………… 89
6.2.7 Inverse Discrete Wavelet Transform…………………………………….…………… 89
6.3 Watermark Detection…………………………………………………………… 89
6.3.1 Pseudo Random Permutation, Block Mapping, and Block Activity index………… 90
6.3.2 XOR Processing………………………………………………..…..………………… 91
6.3.3 Inverse Permutation……………………………………..……..…..………………… 91
6.3.4 Normalized Correlation (NC)…………………………………………...…………… 91
6.4 Experimental Results……………………………………………………………………… 92
6.5 Discussion………… ……………………………………………………………………… 93
7. Conclusion ………………………………………………………………………………… 100
7.1 Summary ……………………………………………………………………………… 100
7.2 Future Work ……………………………………………………………………… 102
Bibliography………………….. ……………………………………………………………… 104

參考文獻

Bibliography
[1] K. R. Rao and J. J. Hwang. Techniques and standards for image, video, and audio coding. Prentice-Hall, 1996.
[2] V. Bhaskaran and K. Konstantinides. Image and video compression standards. Kluwer academic, Hingham, 1995.
[3] I. J. Cox, M. L. Miller, and J. A. Bloom. Digital watermarking. Morgan Kaufmann, 1999.
[4] S. Katzenbeisser and F. A. P. Petitcolas. Information hiding techniques for steganography and digital watermarking. Boston: Artech House, 2000.
[5] I. J. Cox, J. Kilian, T. Leighton, and T. Shamoon, “Secure spread spectrum watermarking for multimedia,” IEEE Trans. Image Processing, vol. 6, pp. 1637-1687, Dec. 1997.
[6] J. T. Tou and R. C. Gonzalez. Pattern recognition principles. Addison-Wesley, Massachusetts, 1974.
[7] H. C. Andrews. Introduction to mathematical techniques in pattern recognition. John Wiley & Sons, New York, 1972.
[8] J. T. Tou. “Feature extraction in pattern recognition,” Pattern Recognition, vol. 1, pp. 3-11, 1968.
[9] JPEG 2000 Part I: Final Draft International Standard (ISO/IEC FDIS15444-1), ISO/IEC JTC1/SC29/WG1 N1855, Aug. 2000.
[10] JPEG 2000 Part I defect report. Technical Report ISO/IEC JTC1/SC29/WG1, January 2001.
[11] D. S. Taubman and M. W. Marcellin. JPEG2000 image compression fundamentals, standards and practice. Kluwer Academic Publishers, 2001.
[12] A. Skodras, C. Cbristopoulos, T. Ebrabimi, “The JPEG2000 still image compression standard,” IEEE Signal Processing Magnzine, vol. 1, pp. 36-58, Sept. 2001.
[13] M. Antonini, M. Barlaud, P. Mathieu and I. Daubechies, “Image coding using wavelet transform,” IEEE Trans. Image Processing, vol. 1, pp. 205-220, April 1992.
[14] G. Strang and T. Nguyen. Wavelet and filter banks. Wellesley-Cambridge Press, Wellesley, 1996.
[15] M. Vetterli and J. Kovacevic. Wavelet and subband coding. Prentice-Hall, New Jersey, 1995.
[16] R. M. Rao and A. S. Bopardikar. Wavelet transforms introduction to theory and applications. Addison-Wesley, 1998.
[17] S. H. Wang and Y. P. Lin, “Wavelet tree quantization for copyright protection watermarking,” IEEE Trans. Image Processing, vol. 13, pp. 154-165, Feb. 2004.
[18] W. Zhu, Z. Xiong and Y. Q. Zhang, “Multiresolution watermarking for images and video,” IEEE Trans. Circuits and Systems for video technology, vol. 9, pp. 545-550, June 1999.
[19] C. T. Hsu and J. L. Wu, “Multiresolution watermarking for digital images,” IEEE Trans. on Circuits and Systems II, vol. 45, pp. 1097-1101, August 1998.
[20] F. Hartung and M. Kutter, “Multimedia watermarking techniques,” in Proc. IEEE, vol. 87, pp. 1079-1107, July 1999.
[21] P. C. Chang, T. T. Lu, and L. L. Lee, “Blockwise image watermarking system with selective data embedding in wavelet transform domain,” in Proc. SPIE Security and Watermarking of Multimedia Contents IV, vol. 4675, pp. 368-377, San Jose, CA, Jan. 2002.
[22] T. T. Lu, L. L. Lee, and P. C. Chang, “A key-based image watermarking system using subblock composition in DCT domain,” in Proc. Multimedia Technology and Applications Conference 2001, pp. 157-163, Irvine, CA, Nov. 2001.
[23] T. T. Lu and P. C. Chang, “Selective block-wise reordering technique for very low bit-rate wavelet video coding,” IEICE Trans. Fundamentals, Vol.E87-A No.4, pp.920-928, April 2004.
[24] T. T. Lu and P. C. Chang, “Low rate video coding with block reordering in wavelet domain,” in SPIE Conf. Electronic Imaging, vol. 5308, pp. 1218-1225, San Jose, CA, Jan. 2004.
[25] P. C. Chang and T. T. Lu, “A scalable video compression technique based on wavelet transform and MPEG coding,” IEEE Trans. Consumer Electronics, vol. 45, No. 3, pp.788-793, Aug. 1999.
[26] P. C. Chang and T. T. Lu, “A scalable video compression technique based on wavelet and MPEG coding,” in Digest of Technical Papers of ICCE '99, pp. 372-373, Los Angeles, CA, June 1999.
[27] D. Taubman, “High performance scalable image compression with EBCOT,” IEEE Trans. Image Processing, vol. 9, pp. 1158-1170, July 2000.
[28] D. Taubman, “High performance scalable image compression with EBCOT,” in Proc. IEEE Int. Conf. Image Processing, pp. 344-348, Oct. 1999.
[29] A. Said and W. A. Pearlman, “A new fast and efficient image codec based on set partitioning in hierarchical trees,” IEEE Trans. Circuits and Syst. Video Technol., vol. 6, pp.243-250, June 1996.
[30] A. Said and W. A. Pearlman, “An image multiresolution representation for lossless and lossy image compression,” IEEE Trans. Image Processing., vol. 5, pp.1303-1310, May 1996.
[31] Information Technology-JPEG-Digital Compression and Coding of Continuous-Tone Still Image-Part 1: Requirements and Guidelines, ISO/IEC 10918-1 and ITU-T Recommendation T.81, 1994.
[32] L. Liu and G. Fan, “A new JPEG2000 region-of-interest image coding method: partial significant bitplanes shift,” IEEE Signal Processing Letters, vol. 10, pp. 35-38, Feb. 2003.
[33] Z. Wang and A. C. Bovik, “Bitplane-by-bitplane shift (BbBshift)-a suggestion for JPEG 2000 region of interest coding, ” IEEE Signal Processing Letters, vol. 9, pp. 160-162, May 2002.
[34] J. M. Shapiro, “Embedded image coding using zerotrees of wavelet coefficients,” IEEE Trans. Signal Processing, vol. 41, pp. 3445-3462, Dec. 1993.
[35] T. T. Lu, K. W. Wen, and P. C. Chang, “Block reordering wavelet packet SPIHT image coding,” in Proc. IEEE Pacific-Rim Conference on Multimedia, pp. 442-449, Oct. 2001.
[36] K. Sayood. Introduction to data compression. Morgan Kaufmann, San Francisco, 2000.
[37] B. J. Kim, Z. Xiong and W. A. Pearlman, “Low bit-rate scalable video coding with 3-D set partitioning in hierarchical trees,” IEEE Trans. on Circuits and Syst. Video Technol., vol. 10, pp. 1374-1387, Dec. 2000.
[38] B. J. Kim and W. A. Pearlman, “An embedded video wavelet coder using three-dimensional set partitioning in hierarchical tree,” in Proc. Data Compression Conf. pp. 251-260, 1997
[39] E. Khan and M. Ghanbari, “Very low bit rate video coding using virtual SPIHT,” Electronic Letters, vol. 37, pp. 40-41, Jan. 2001.
[40] K. K. Lin and R. M. Gray, “Video residual coding using SPIHT and dependent optimization,” in Proc. Data Compression Conf. pp. 113-122, 2001.
[41] I. H. Witten, R. M. Neal, and J. G. Cleary, “Arithmetic coding for data compression,” Commun. ACM, vol. 30, pp. 520-540, June 1987.
[42] S. G. Mallat, “A theory for multiresolution signal decomposition: The wavelet representation,” IEEE Trans. Patt. Analysis and Mach. Intell., vol. 11,pp. 674-693, July 1989.
[43] S. Zafar, Y. Zhang, and B. Jabbari, “Multiscale Video Representation Using Multiresolution Motion Compensation and Wavelet Decomposition,” IEEE Journal on Selected Areas in Communications, vol. 11, pp. 24-35, Jan. 1993.
[44] H. W. Paik and A. Khubchandani, “Quantization Scheme for JPEG Baseline Sequential Encoding of Still Images,” Proceeding of 35th Midwest Symposium on Circuit and System, vol. 2, pp. 976-979, 1992.
[45] G. Strang and T. Nguyen. Wavelets and Filter Banks, Wellesley Cambridge, 1996.
[46] M. A. Suhail and M. S. Obaidat, “Digital watermarking-based DCT and JPEG model,” IEEE Trans. Instrumentation and Measurement, vol. 52, pp. 1640-1647, Oct. 2003.
[47] A. Nikolaidis and I. Pitas, “Region-based image watermarking,” IEEE Trans. Image Processing, vol. 70, pp. 726-738, Nov. 2001.
[48] S. Katzenbeisser and F. A. P. Petitcolas, Information Hiding Techniques for Steganography and Digital Watermarking. Boston: Artech House, 2000.
[49] W. C. Chu, “DCT-based image watermarking using subsampling,” IEEE Trans. Multimedia , vol. 5, pp. 34-38, March 2003.
[50] C. T. Hsu and J. L. Wu, “Hidden digital watermarks in images,” IEEE Trans. Image Processing, vol. 8, pp. 58-68, January 1999.
[51] B. Chen and G. W. Wormell, “Quantization index modulation: a class of provably good methods for digital watermarking and information embedding,” IEEE Trans. Information Theory, vol. 47, pp. 1423-1443, May 2001.
[52] P. W. Wong, O. C. Au, and Y. M. Yeung, “A novel blind multiple watermarking technique for images,” IEEE Trans. Circuits and Systems for video technology, vol. 13, pp. 813-830, August 2003.
[53] P. W. Wong, and N. Memon, “Secret and public key image watermarking schemes for image authentication and ownership verification,” IEEE Trans. Image Processing, vol. 10, pp. 1593-1601, Oct. 2001.
[54] S. Voloshynovskiy, S. Pereira, V. Iquise, and T. Pun, “Attack modeling: towards a second generation watermarking benchmark,” Signal Processing, pp. 1177-1214, 2001.

指導教授

張寶基(Pao-Chi Chang)

審核日期

2004-7-8

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