在多重敘述視訊編碼架構下之動態衰減預測技術

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研究生：	賴宏亮 Hung-Liang Lai
論文名稱：	在多重敘述視訊編碼架構下之動態衰減預測技術 Adaptive Leaky Prediction Technique under Multiple Description Video Coding Framework
指導教授：	陳永昌 Yung-Chang Chen
口試委員:
學位類別：	碩士 Master
系所名稱：	電機資訊學院 - 電機工程學系 Department of Electrical Engineering
論文出版年：	2005
畢業學年度：	93
語文別：	英文
論文頁數：	56
中文關鍵詞：	衰減預測、多重敘述、視訊編碼、串流視訊、無線網路
外文關鍵詞：	Leaky Prediction, Multiple Description, Video Coding, Streaming Video, Wireless Network
相關次數：	點閱：3 下載：0
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近年來，隨著網路的發展，經由封包遺失網路的視訊串流技術已經受到很多注意。但是封包遺失的問題依然是難以處理的。因為視訊編碼使用運動補償預測技術，所以如果前一張畫面的封包有遺失，在解碼時將存在錯誤傳遞的問題。為了要去解決錯誤傳遞的問題，衰減預測技術是有名的方法。然而如何在衰減預測裡去找到一個動態最佳的衰減因素依然是一個有挑戰性的工作。對於這樣的一個工作，我們在基本多重敘述的架構下對於衰減預測裡的衰減因素提出一個新的解決方法。
大體而言，我們的最佳衰減因素依然決定於封包遺失比率。但是視訊的自然性質例如複雜度或運動性也會影響衰減因素的決定。複雜度高或是運動量大的視訊畫面在相同遺失比率下需要較小的衰減因素，而複雜度低或運動量小的視訊畫面則只需要用較大的衰減因素。並且討論整個編解碼器的架構是必要的，因為編解碼器的一些特性例如多重敘述編碼或錯誤隱藏技術將有助於遺失畫面的重建，也就是說如果我們可以藉由某些技術去重建回遺失的畫面，此時錯誤傳遞的問題將減輕，而我們可使用較大的衰減因素，所以考量整個架構中有助於遺失畫面的重建的技術所帶來的影響是必要的。因此，我們提出一個關於如何找到最佳衰減因素的方法，決定於遺失比率，視訊的自然性質跟整個編解碼架構。
根據實驗結果，我們提出的演算法在相同的封包遺失比率下比其他衰減因素提供較佳的效能，而且我們也證明在我們演算法中提出的參數是必須去考量的。也就是在決定最佳的衰減因素時，我們必須考慮視訊的自然性質跟整個編解碼架構。

In recent years, with the development of the internet, video streaming across packet-lossy networks has received much attention. But the problem of packet loss is still difficult to deal with. Since video coding uses motion compensation prediction, there will be error propagation problems in the decoding if some packets of previous frames are lost. To solve the problem of the error propagation, the well-known approach is leaky prediction. However, how to find an adaptive optimal leaky factor in the leaky prediction remains a challenging task. For such a task, we propose a new solution for the leaky factor of leaky prediction under Base MDSQ.
Generally speaking, our optimal leaky factor is still depending on the packet loss rate. But the natural property of videos such as the complexity or amount of movement also affects the decision of the leaky factor. Moreover, it is necessary that the whole framework of coder must be considered because some properties of coder such as multiple description coding or Error Concealment technique can help for the reconstruction of lost frames. Therefore, we propose a new method about how to find the optimal leaky factors depending on the loss rate, the natural property of videos and the whole framework of the coder.
From the simulation results, we can see that the proposed algorithm provides better performance than other leaky factors. And our method can have fine performance for the videos with different properties.

Chapter 1: Introduction 1
         1.1  The Challenge of Streaming Video 1
         1.2  Motivation 2
         1.3  Thesis Organization 2

Chapter 2: Overview of Base MDSQ with Leaky Prediction    4
    2.1 Leaky Prediction for Layer Coding 4
    2.2 Multiple Description Scalar Quantization 6
    2.2.1 The Encoding Technique about MDSQ 6
    2.2.2 The Index Assignment Matrix 6
    2.2.3 The Decoding Technique about MDSQ 8
    2.3 Base MDSQ with Leaky Prediction 9
         2.3.1 The Definition of Base MDSQ 9
         2.3.2 The Modification of Index Assignment Matrix
              for Base MDSQ 11
         2.4 Architecture of Leaky Prediction for Base MDSQ
             Video Coding 14
         2.5 Video Coding Using Leaky Prediction 15

Chapter 3: Adaptive Leaky Prediction Technique under Base
           MDSQ 18
    3.1 Algorithm of the Proposed Method 18
    3.1.1 The Leaky Factor in Base Part 19
    3.1.2 The Leaky Factor in Enhancement Part 22
    3.2 Definition of The Parameters 26
    3.2.1 The Weighting Value 26
    3.2.2 The r Value 29
    3.2.3 The B Value 31
         3.3 Summary 33

Chapter 4: Simulation Results 34
    4.1 Simulation Environments     34
    4.2 Simulation Results 39
         4.2.1 The Performance of Proposed factor 41
         4.2.2 The Performance of Proposed factor 42
         4.2.3 The Performance of Proposed Algorithm 44
    4.3 Effect of the parameters 48
    4.3.1 Effect of The Weighting Value 48
    4.3.2 Effect of B    49
    4.3.3 Effect of r 50
    4.4 Summary 52

Chapter 5: Conclusions and Future Works 53
    5.1 Conclusions 53
    5.2 Future Works 54
Reference 55

                                

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