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研究生:魯大德
研究生(外文):Ta-Te Lu
論文名稱:區塊特徵應用於影像視訊壓縮及浮水印之研究
論文名稱(外文):Featured-based block-wise processing applied to image and video compression and watermarking systems
指導教授:張寶基
指導教授(外文):Pao-Chi Chang
學位類別:博士
校院名稱:國立中央大學
系所名稱:電機工程研究所
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2004
畢業學年度:92
語文別:英文
論文頁數:110
中文關鍵詞:小波轉換區塊重組浮水印資料壓縮
外文關鍵詞:reorderingdata compressionimage watermarkingJPEG2000SPIHTbit-plane clustering
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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。
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.
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
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