可調式視訊編碼提供了良好的編碼彈性，藉由擷取部分位元流的方式來滿足在異質網路頻寬傳輸以及在不同需求的多媒體裝置播放視訊影像，但是目前大多數的視訊內容的格式皆為不可調適，如：H.264/AVC。因此將傳統不可調適的視訊格式有效率地轉碼成可調式視訊格式是急需被發展的，然而將傳統視訊格式轉碼成可調適視訊格式面臨龐大的計算複雜度和不同的編碼架構。本篇論文提出了快速視訊轉碼架構克服了H.264/AVC和SVC不同的編碼架構，此演算法著重在簡化模式決策程序中的計算複雜度。首先從H.264/AVC的視訊串流擷取H.264/AVC的模式，透過事先訓練好的條件機率來決定在SVC中的模式決策，接著貝氏定理用來檢測錯誤的模式決策的發生，最後利用馬可夫模型使模式決策之預測更為準確。實驗結果顯示本演算法相對於串聯式像素值域轉碼器能節省75.28%的編碼時間，並在畫面品質上只損失0.13dB。

Scalable video coding (SVC) supports full scalability by extracting a partial bitstream to adapt to transmission and display requirements in multimedia applications. Most conventional video content is stored in non-scalable format, e.g., H.264/AVC, necessitating the development of an efficient video transcoding from a conventional format to a scalable one. This work describes a fast video transcoding architecture that overcomes the complexity of different coding structures between H.264/AVC and SVC. The proposed algorithm simplifies the mode decision process in SVC owing to its heavy computations. The current mode in SVC is selected by the highest conditional probability of SVC’s mode given the H.264/AVC’s mode. Exactly when an error prediction occurs is then detected using Bayesian theorem, followed by its refinement using the Markov model. Experimental results indicate that the proposed algorithm saves on average 75.28% of coding time with 0.13 dB PSNR loss over that when using a cascaded pixel domain transcoder.

中文摘要 i
Abstract ii
Contents iii
List of Figures iv
List of Tables v
Chapter 1 Introduction 1
1.1 Overview of Video Transcoding 1
1.2 Overview of H.264/Advance Video Coding 6
1.3 Overview of Scalable Video Coding 11
1.4 Motivation 16
1.5 Contribution 18
1.6 Organization 19
Chapter 2 Background Review 20
2.1 Previous Works in Video Transcoding from H.264/AVC to SVC 21
2.2 Fast Inter Mode Decision Algorithm Based on the MB Activity for MPEG-2 to H.264/AVC Transcoding 23
2.3 MPEG-4 to H.264 Transcoding using Macroblock Statistics 26
Chapter 3 Proposed Video Transcoding from H.264/AVC to SVC 29
3.1 Candidate Mode Selection through Conditional Probability 32
3.2 Mode Testing by Bayesian Theorem Detection 36
3.3 Mode Refinement by Using the Markov Chain 41
3.4 Summary 47
Chapter 4 Experimental Results 49
4.1 Parameter Setting 51
4.2 Performance Comparison 53
Chapter 5 Conclusions and Future Works 73
5.1 Conclusion 73
5.2 Future Work 75
Reference 76

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