臺灣博碩士論文加值系統

H.264/AVC是目前最先進的視訊壓縮編碼標準，無論在視訊品質以及編碼效率上，都比其他既存的標準，像MPEG-4和H.263來得出色。此標準採用許多新的技術，如訊框內預測(intra prediction)、訊框間預測(inter prediction)、整數離散餘弦轉換(Integer Discrete Cosine Transform, Integer DCT)，大大地運用高計算複雜度來降低空間與時間上的冗餘訊息(residual message)。數位浮水印技術用來解決驗證視訊內容版權的問題，而其大多數通常針對嵌入後的視訊品質作討論，比較少探討編碼效率。因此本篇論文提出了一個數位浮水印技術，透過預測與原始所產生的差值巨方塊(residual macroblock)，進行浮水印的嵌入與萃取。方法分為三部分做處理：訊框內巨方塊模式(Inter_MB mode)、訊框間巨方塊模式(Inter_MB mode)以及訊框內訊框間巨方塊合併模式(Intra_MB&Inter_MB mode)。
最後將演算法實現在H.264編解碼器JM8.6。透過實驗證明，本文所提出的演算法，簡單可以達成，並且可以直接在解碼端萃取出浮水印，而不需要原始視訊的配合，有效地對H.264視訊內容達到驗證的功能，編碼效率也會熵編碼的特性，因而有所提升。

H.264/AVC is the currently state-of-the-art video compression standard performing significant improvement in coding efficiency and quality compared to other existing standard, such as MPEG-4 and H.263. The standard adapts a lot of novel techniques, like intra prediction, inter prediction and Integer DCT (Integer Discrete Cosine Transform), to exploit high operational complexity for reducing spatial and temporal messages. To authenticate the content of video sequences, many watermarking scheme have been investigated to verify the integrity of video sequence. However, most of them focus on the quality of the watermarked video rather than the coding efficiency. Accordingly, in the paper a digital watermarking scheme is proposed based on the residual macroblock which is the residues between actual macroblock and predicted macroblock. The proposed algorithm is segmented into three modes: Intra_MB mode, Inter_MB mode and Intra_MB&Inter_MB mode for embedding and extracting.
The proposed watermarking algorithm is implemented and tested in the emerging H.264 encoder reference software JM8.6. Experiments show that the proposed watermarking algorithm is easily achieved and the watermark can be directly extracted at the decoder side without the original video. It provides a good solution for H.264 video stream authentication and the coding efficiency will increase accordingly because of the characteristics of the entropy coding.

第一章 緒論 1
1.1 研究背景與動機 1
1.2 H.264/AVC視訊編碼壓縮標準 2
1.2.1 檔案規模(Profile) 3
1.2.2 編碼器架構(Encoder scheme) 4
1.2.3 解碼器架構(Decoder scheme) 6
1.2.4 片組(Slice) 7
1.2.5 整數離散餘弦轉換(Integer Discrete Cosine Transform) 8
1.2.6 量化(Quantization) 13
1.3 數位浮水印技術 17
1.3.1 浮水印處理階段 17
1.3.2 浮水印種類 19
1.3.3 浮水印特性 19
1.4 相關研究與文獻 20
1.5 文章架構 21

第二章 浮水印演算法 22
2.1 RGB轉YUV 22
2.2 差值巨方塊(Residual Macroblock) 23
2.3 Intra_MB模式浮水印演算法 27
2.3.1 嵌入(Embedding) 27
2.3.2 萃取(Extracting) 30
2.4 Inter_MB模式浮水印演算法 33
2.4.1嵌入(Embedding) 33
2.4.2萃取(Extracting) 36
2.5 浮水印演算法的編碼效率 39

第三章 實驗模擬結果 41
3.1 視訊品質評估 43
3.1.1 Intra_MB模式 43
3.1.2 Inter_MB模式 50
3.2 QP值與PSNR關係 57
3.2.1 Intra_MB模式 57
3.2.2 Inter_MB模式 61
3.3 CRI (compression Ratio Improvement) 64
3.3.1 PSNR與CRI關係 65
3.3.2 QP與CRI關係 66
3.3.3 CR與CRI關係 68
3.4 Intra_MB模式以及Inter_MB模式分析 70
3.5 Inra_MB與Inter_MB合併模式 71
3.5.1 視訊品質 71
3.5.2 QP與PSNR關係 78
3.5.3 PSNR與CRI關係 81
3.5.4 QP與CRI關係 86
3.5.5 CR與CRI關係 90
3.6 攻擊(Attack) 94
3.6.1 雜訊 94
3.6.2 再壓縮處理 94
3.6.3 移除GOP 95
3.6.4 取代GOP 96

第四章 結論 97

參考文獻 99
論文發表 103

圖1.1 H.264/AVC發展歷史........................3
圖1.2 H.264/AVC Profile支援範圍............... 4
圖1.3 H.264/AVC編碼流程概念圖...................5
圖1.4 H.264/AVC解碼流程概念圖...................6
圖1.5 H.264/AVC編碼處理層次示意圖...............7
圖1.6 視訊Akiyo控制QP下的擷取影像...............17
圖2.1 視訊壓縮處理圖...........................22
圖2.2 訊框內預測後的差值巨方塊架構圖.............24
圖2.3 訊框間預測後的差值巨方塊架構圖............ 24
圖2.4 浮水印嵌入架構圖.........................25
圖2.5 浮水印萃取架構圖.........................26
圖2.6 Intra_MB模式之浮水印嵌入架構圖............27
圖2.7 Intra_MB模式嵌入流程圖...................29
圖2.8 Intra_MB模式之浮水印萃取架構圖............30
圖2.9 Intra_MB模式萃取流程圖...................32
圖2.10 Inter_MB模式之浮水印嵌入架構圖...........33
圖2.11 Inter_MB模式嵌入流程圖..................35
圖2.12 Inter_MB模式之浮水印萃取架構圖.......... 36
圖2.13 Inter_MB模式萃取流程圖..................38
圖2.14浮水印演算法之圖例說明....................40
圖3.1 測試影像...............................42
圖3.2 Akiyo測試影像(a)without watermark(b)with watermark.......44
圖3.3 Akiyo測試數據........................... 44
圖3.4 Bus測試影像(a)without watermark(b)with watermark.......45
圖3.5 Bus測試數據............................. 45
圖3.6 Foreman測試影像(a)without watermark(b)with watermark......46
圖3.7 Foreman測試數據...............46
圖3.8 Mobile測試影像(a)without watermark(b)with watermark.............47
圖3.9 Mobile測試數據............... 47
圖3.10 Stefan測試影像(a)without watermark(b)with watermark.............48
圖3.11 Stefan測試數據.............. 48
圖3.12 Tempete測試影像(a)without watermark(b)with watermark.............49
圖3.13 Tempete測試數據............. 49
圖3.14 Akiyo測試影像(a)without watermark(b)with watermark............ 51
圖3.15 Akiyo測試數據............... 51
圖3.16 Bus測試影像(a)without watermark(b)with watermark............ 52
圖3.17 Bus測試數據................. 52
圖3.18 Foreman測試影像(a)without watermark(b)with watermark............ 53
圖3.19 Foreman測試數據............. 53
圖3.20 Mobile測試影像(a)without watermark(b)with watermark............ 54
圖3.21 Mobile測試數據.............. 54
圖3.22 Stefan測試影像(a)without watermark(b)with watermark............ 55
圖3.23 Stefan測試數據.............. 55
圖3.24 Tempete測試影像(a)without watermark(b)with watermark.............56
圖3.25 Tempete測試數據............. 56
圖3.26 Akiyo在控制QP範圍從0至51，對應的PSNR值..... 58
圖3.27 Bus在控制QP範圍從0至51，對應的PSNR值........58
圖3.28 Foreman在控制QP範圍從0至51，對應的PSNR值....59
圖3.29 Mobile在控制QP範圍從0至51，對應的PSNR值.....59
圖3.30 Stefan在控制QP範圍從0至51，對應的PSNR值.....60
圖3.31 Tempete在控制QP範圍從0至51，對應的PSNR值....60
圖3.32 Akiyo在控制QP範圍從0至51，對應的PSNR值......61
圖3.33 Bus在控制QP範圍從0至51，對應的PSNR值........62
圖3.34 Foreman在控制QP範圍從0至51，對應的PSNR值....62
圖3.35 Mobile在控制QP範圍從0至51，對應的PSNR值.....63
圖3.36 Stefan在控制QP範圍從0至51，對應的PSNR值.....63
圖3.37 Tempete在控制QP範圍從0至51，對應的PSNR值....64
圖3.38 操作在Intra_MB模式，PSNR與CRI相對關係數據圖.65
圖3.39 操作在Inter_MB模式，PSNR與CRI相對關係數據圖.66
圖3.40 操作在Intra_MB模式，QP與CRI相對關係數據圖...67
圖3.41 操作在Inter_MB模式，QP與CRI相對關係數據圖...68
圖3.42 操作在Intra_MB模式，CR與CRI相對關係數據圖...69
圖3.43 操作在Inter_MB模式，CR與CRI相對關係數據圖...70
圖3.44 Akiyo在合併模式測試影像(a)without watermark(b)with watermark... 72
圖3.45 Akiyo測試數據..........................72
圖3.46 Bus在合併模式測試影像(a)without watermark(b)with watermark....73
圖3.47 Bus測試數據...................... 73
圖3.48 Foreman在合併模式測試影像(a)without watermark(b)with watermark 74
圖3.49 Foreman測試數據...................74
圖3.50 Mobile在合併模式測試影像(a)without watermark(b)with watermark.. 75
圖3.51 Mobile測試數據....................75
圖3.52 Stefan在合併模式測試影像(a)without watermark(b)with watermark... 76
圖3.53 Stefan測試數據....................76
圖3.54 Tempete在合併模式測試影像(a)without watermark(b)with watermark 77
圖3.55 Tempete測試數據...................77
圖3.56 Akiyo在三種模式下，控制QP範圍從0至51，其對應的PSNR值....... 78
圖3.57 Bus在三種模式下，控制QP範圍從0至51，其對應的PSNR值.. 79
圖3.58 Foreman在三種模式下，控制QP範圍從0至51，其對應的PSNR值79
圖3.59 Mobile在三種模式下，控制QP範圍從0至51，其對應的PSNR值..80
圖3.60 Stefan在三種模式下，控制QP範圍從0至51，其對應的PSNR值..80
圖3.61 Tempete在三種模式下，控制QP範圍從0至51，其對應的PSNR值.81
圖3.62 操作在合併模式，PSNR與CRI相對關係數據圖.........82
圖3.63 Akiyo在三種模式下，PSNR與CRI相對關係數據圖.....83
圖3.64 Bus在三種模式下，PSNR與CRI相對關係數據圖.......83
圖3.65 Foreman在三種模式下，PSNR與CRI相對關係數據圖...84
圖3.66 Mobile在三種模式下，PSNR與CRI相對關係數據圖....84
圖3.67 Stefan在三種模式下，PSNR與CRI相對關係數據圖....85
圖3.68 Tempete在三種模式下，PSNR與CRI相對關係數據圖..85
圖3.69 操作在合併模式，QP與CRI相對關係數據圖.........86
圖3.70 Akiyo在三種模式下，QP與CRI相對關係數據圖......87
圖3.71 Bus在三種模式下，QP與CRI相對關係數據圖........87
圖3.72 Foreman在三種模式下，QP與CRI相對關係數據圖....88
圖3.73 Mobile在三種模式下，QP與CRI相對關係數據圖.....88
圖3.74 Stefan在三種模式下，QP與CRI相對關係數據圖.....89
圖3.75 Tempete在三種模式下，QP與CRI相對關係數據圖....89
圖3.76 操作在合併模式，CR與CRI相對關係數據圖.........90
圖3.77 Akiyo在三種模式下，CR與CRI相對關係數據圖......91
圖3.78 Bus在三種模式下，CR與CRI相對關係數據圖........91
圖3.79 Foreman在三種模式下，CR與CRI相對關係數據圖....92
圖3.80 Mobile在三種模式下，CR與CRI相對關係數據圖.....92
圖3.81 Stefan在三種模式下，CR與CRI相對關係數據圖.....93
圖3.82 Tempete在三種模式下，CR與CRI相對關係數據圖....93
圖3.83 雜訊攻擊下，萃取出的浮水印.................94
圖3.84 再壓縮攻擊下，萃取出的浮水印................95
圖3.85 移除GOP攻擊下，萃取出的浮水印.............. 96
圖3.86 取代GOP攻擊下，萃取出的浮水印.............. 96

表1.1 H.264/AVC邊解碼器的量化步長................14
表1.2 H.264/AVC MF值...........................16
表3.1 JM實驗模擬相關參數設定............................................41

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