臺灣博碩士論文加值系統

位元率控制 (Rate control) 在視訊編碼和多媒體串流上扮演相當重要的角色。
一個好的位元率控制機制能夠 1)在有限的編碼預算位元下，有效地調整量化參數，以達到一個好的壓縮畫面品質，以及 2)在所估測的可用頻寬下，能夠調整影音資料的傳輸速率，避免網路的擁塞狀況。在本論文中，我將設計與分析位元率控制在H.264 視訊編碼與在異質網路上之傳輸應用，以下包含了三個重要的議題: 位元率分配與量化參數決定(編碼階段)，頻寬管理與可調式 (scalable) 影音串流同步(串流階段)。
在編碼階段的位元率控制中，我將提出一個利用4-D 以人類感知為基礎之量化模型，包含二個主要模組: 1-D 畫面層上的位元率分配與3-D 巨方塊層(macroblock) 上的量化參數決定機制。一維的時間模式 (temporal pattern) 主要用來估測畫面的複雜度與決定編碼的預算位元率; 三維的位元率模式 (rate pattern)，則是用來建構一個三維的位元率-複雜度-量化模型。其中，此模型的曲線斜率被視為一個決定量化參數的重要因素。此外，為了符合新進畫面的特性，我將利用一個加權最小平方估測 (weighted least square estimation) 來更新此模型的相關係數。
在串流階段的位元率控制中，為了配合時變的頻寬來調整視訊串流的傳輸速率，一個可預測之隨選視訊頻寬管理與回饋式的緩衝區設計是有必要性的。我將根據所量測到的封包往返時間、遺失率、延遲抖動 (delay jitter) 與所接收的位元數，利用卡曼濾波器(Kalman filter) 來估測目前的有效頻寬，並根據解碼端的緩
衝區狀況來調整傳輸位元率。卡曼濾波器中的狀態轉移矩陣 (transition matrix)、錯誤共變異數 (error covariance)，將會被適當地初始化、收歛以配合目前網路的特性。
除了上述的頻寬管理外，可調式影音串流同步也是我們所關心的議題之一。由於異質網路與設備能力的多樣性，串流影音的品質應該能夠被適當地調整。為達到此目的，在過去的可調式多媒體編碼方法，包括FGS (fine-granular scalability)與BSAC (bit-sliced arithmetic coding)，能夠將視訊、音訊資料切割成一個基礎層與多個加強層。考量可調式影音編碼特性、可用頻寬和視訊音訊解碼複雜度，我將設計包含消除延遲抖動機制，條件式的重傳機制與撥放同步機制，來同步地傳輸多階層可調式的影音串流資料。
最後，實驗結果將呈現 1) 所提出之H.264 的位元率控制，相較H.264 JM10.2，能夠有效地維持穩定編碼緩衝區，並改善影像品質; 2) 利用卡曼濾波器所設計的
頻寬管理機制，能夠得到較精確的頻寬大小，並減少串流封包在有線/無線/3G 網路上的遺失率; 3) 所提出的可調式影音串流同步機制能有效地減少延遲抖動的影響，並提高即時 (in-time) 解碼與影音同步撥放的比率。

Bit-rate control plays a major role in video coding and multimedia streaming. Well-designed
bit-rate control mechanisms are able to 1) efficiently adjust encoding quantizers for achieving fine visual qualities in subject to encoding bit budgets and 2) adjust the transmission rate for avoiding network congestion in subject to the predicted available bandwidth. In the dissertation, bit-rate control
mechanisms for H.264 video coding and ransmission over heterogeneous networks are designed and analyzed, including three critical issues: bit allocation and quantizers decision (at the encoding stage), bandwidth management and scalable audiovisual streaming synchronization (at the streaming stage).

For encoding-stage bit-rate control, H.264 bit-rate control using a 4-D perceptual quantization
modeling (PQrc) is proposed, including two major encoding modules: the perceptual frame-level
bit-allocation using a 1-D temporal pattern and the macroblock-level quantizer decision using a 3-D rate pattern. The temporal pattern is used to predict frame complexity and determine proper bit budgets further. The rate pattern is depicted as a bit-complexity-quantization (B.C.Q.) model, in which a tangent slope of a B.C.Q. curve is a piece of unique information to find a proper quantizer.
For newly generated video clips, the B.C.Q. model can be updated continuously using a weighted least square estimation.

For streaming-stage bit-rate control, in order to adapt the transmission rate of streamed ondemand videos to the time-varying channel bandwidth, predictive video-on-demand (VoD) bandwidth management and a feedback-based buffer control scheme are required. According to the measured information of packet round-trip-time, loss-rate, delay jitter and received bit-rate, an improved Kalman filter is adopted to predict an available channel bandwidth recursively and to determine a proper transmission rate in consideration of buffer fullness of a decoder. The optimal parameters of the Kalman filter, e.g., a transition matrix and error covariances, can be initialized, converged and adapted to
characteristics of the current network.

In addition to the aforementioned bandwidth management, scalable audiovisual streaming synchronization is also a main issue we concern. Considering characteristics of heterogeneous networks and device capabilities, streamed audiovisual content is requested to be scaled with the proper spatial/temporal resolution, format and quality levels. Past technologies of scalable audiovisual coding, e.g., Fine-granular scalability (FGS) and bit-sliced arithmetic coding (BSAC), are used to segment video and audio data into one base-layer and multiple enhancement-layer bitstreams. With the advantages
of scalable audiovisual coding, a de-jitter procedure, a conditional retransmission mechanism
and a playout synchronization mechanism are designed to transmit hybrid scalable (multi-layered) audiovisual bitstreams in consideration of the result of a network bandwidth adaptation and distinct decoding time-complexity.
Finally, experimental results show that 1) the proposed H.264 rate control can keep stable buffer fullness and improve the SNR quality and control accuracy effectively in comparison with H.264 JM10.2; 2) the proposed bandwidth management using the improved Kalman filter can obtain more precise and stable estimation results of bandwidth in comparison with pathChirp and decrease the packet loss-rate when data are streamed in real wired/WiFi/3G networks; 3) the proposed scalable
audiovisual streaming synchronization can eliminate effects of the delay jitter, increase the in-time decoding ratio and perceive audiovisual playout smoothly.

Contents
1 Introduction 1
1.1 Rate Control Issues for H.264 Video Coding . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Rate Control Issues for Video Transmission . . . . . . . . . . . . . . . . . . . . . . 3
1.3 Rate Control Issues for Scalable Audiovisual Streaming Synchronization . . . . . . . 4
1.4 Dissertation Organization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
2 Preliminary 7
2.1 Traditional Rate Control Model for H.264 Video Coding . . . . . . . . . . . . . . . 7
2.2 Well-known Approaches of Bandwidth Management . . . . . . . . . . . . . . . . . 10
2.3 The Fine-Granular-Scalable Audio/video and Quality Evaluation . . . . . . . . . . . 11
3 Design and Analysis of Rate Control for H.264 Video Coding 14
3.1 The Architecture of the 4-D Perceptual Quantization Modeling . . . . . . . . . . . . 14
3.1.1 The Construction of the 1-D Energy Transition Table . . . . . . . . . . . . . 14
3.1.2 The Construction of the 3-D Bit-Complexity-Quantization Model . . . . . . 16
3.2 The Proposed H.264 Perceptual Frame-level Bit-allocation . . . . . . . . . . . . . . 18
3.3 MB Level Quantizer Decision Using the 3-D Bit-Complexity-Quantization Model . . 22
3.3.1 The Control Scheme of the MB Level Quantizer Decision . . . . . . . . . . 22
3.3.2 Update the B.C.Q. Model Using the Weighted Least Square Estimation . . . 25
3.4 Experimental Results and Discussions . . . . . . . . . . . . . . . . . . . . . . . . . 26
3.4.1 The Performance of MAD Estimation Accuracy . . . . . . . . . . . . . . . . 28
3.4.2 The Comparison of Overall Coding Performances . . . . . . . . . . . . . . . 29
3.4.3 The Discussion of the Performance Analysis . . . . . . . . . . . . . . . . . 33
4 Design and Analysis of Rate Control for Video Transmission over Heterogeneous Networks
35
4.1 Estimation Theory- the Kalman filter . . . . . . . . . . . . . . . . . . . . . . . . . . 35
4.2 The Proposed System Architecture with U-BEKF . . . . . . . . . . . . . . . . . . . 36
4.3 The Proposed Bandwidth Management of U-BEKF for Video Transmission . . . . . 39
4.3.1 The Complete Procedure of U-BEKF: Network Monitoring and Bandwidth
Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
4.3.2 The Update Mechanisms of Noise Covariances Q and R . . . . . . . . . . . 43
4.3.3 Rate Adaptation and Feedback-based Buffer Control with U-BEKF . . . . . 45
4.4 Experimental Results and Discussions . . . . . . . . . . . . . . . . . . . . . . . . . 49
4.4.1 The Configurations of the Real Network and the Isolated Network-simulation
Environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
4.4.2 Performance Analyses of U-BEKF . . . . . . . . . . . . . . . . . . . . . . . 50
4.4.3 The Comparisons with pathChirp . . . . . . . . . . . . . . . . . . . . . . . 53
4.4.4 Discussions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55
5 Design and Analysis of Rate Control for Scalable Audiovisual Streaming Synchronization
57
5.1 The System Architecture of the Proposed Multi-layered Audiovisual Streaming Synchronization
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
5.2 The Adaptive Layered Audiovisual Transmission Scheme Using Network Bandwidth
Adaptation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
5.3 The Two-phase Streaming and Playout Synchronization Scheme . . . . . . . . . . . 62
5.3.1 The Phase-I Streaming Synchronization: De-Jitter Procedure . . . . . . . . . 62
5.3.2 The Phase-I streaming Synchronization: Conditional Retransmission Mechanism
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65
5.3.3 The Phase-II Playout Synchronization . . . . . . . . . . . . . . . . . . . . . 68
5.4 Experimental Results and Discussions . . . . . . . . . . . . . . . . . . . . . . . . . 72
5.4.1 The Configuration of the Pure Experiment Environment and the Utilization
of Network Bandwidth Adaptation (NBA) . . . . . . . . . . . . . . . . . . . 73
5.4.2 Evaluation on the Phase-I Synchronization . . . . . . . . . . . . . . . . . . 73
5.4.3 Evaluation on the Phase-II on-time Playout Synchronization . . . . . . . . . 78
6 Conclusion and Future Works 80

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