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研究生： 吳靖宇
Ching-Yu Wu
論文名稱： 基於內容適應式模型之H.264/AVC快速演算法與位元率控制
Model-Based Fast Algorithm and Rate Control for H.264/AVC
指導教授： 蘇柏齊
Po-Chyi Su
口試委員:
學位類別： 博士
Doctor
系所名稱： 資訊電機學院 - 資訊工程學系
Department of Computer Science & Information Engineering
論文出版年： 2014
畢業學年度： 102
語文別： 英文
論文頁數： 129
中文關鍵詞： H.264/AVC 、畫面內預測 、位元率控制 、品質控制 、失真－量化 、位元率－量化 、感興趣區域
外文關鍵詞： H.264/AVC, Intra Prediction, Rate Control, Quality Control, Distortion-Quantization, Rate-Quantization, Region of Interest
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    • H.264/AVC影片編碼已經成為目前最常被使用的編碼標準，許多研究也因此致力於追求更高效率的影片編碼，以及維持良好的位元率－失真表現。在本篇論文中，我們研發了許多內容適應性的模型來加快H.264/AVC的編碼速度，以及達到更好的位元率和品質控制。

    本論文由三個部分組成。首先，我們提出了一個畫面內預測的快速演算法。區塊邊緣的重建像素以及區塊內的內容，會先經由投影的方式產生兩組向量，再進一步計算出預測區塊冗餘。此一特徵值能夠有效地先行刪除一些較可能被位元－失真最佳化所刪除的預測模式，來提升編碼速度。根據預測區塊冗餘以及其他在編碼過程中擷取的資訊，本研究提出了更進一步的跳躍方法來跳過某些編碼模式和區塊種類，使得畫面內編碼能進一步加速。

    再者，增加了畫面內編碼的效率之後，我們探討畫面內編碼的位元－量化關係。如何適當地分配量化參數給Ｉ－畫面，對於影片編碼來說相當重要。我們提出了一個內容適應性的位元－量化模型，來預測Ｉ－畫面的的位元使用量。藉由分析量化參數以及區塊複雜度之間的關係，在目標位元率決定之後，決定一個適合的量化參數來進行編碼。由於提出的模型是建構在巨區塊層，感興趣區域可以藉此使用較多的位元、以及較低的量化參數來編碼，進而達到提升人眼的視覺品質。

    最後，我們藉由估測失真與量化參數間的關係，進一步探討位元率控制以及品質控管問題。我們提出了一個內容適應性的失真－量化模型，來預測畫面或區塊的失真程度。和之前的位元－量化模型類似，該模型只有一個可使用巨區塊內容調整的參數，並且能在每張畫面被編碼之前，就先行預測該畫面的失真程度。在由訊噪比所定義的畫面品質被設定之後，該模型能夠幫助計算出適合的量化參數。藉由此模型，我們進一步探討兩個恆定畫質的影片編碼應用，希望能幫助相關專業應用，例如影片的儲存以及編輯，達到更好的品質與效果。

    H.264/AVC has become the most frequently used video codec nowadays. A lot of efforts have been made to pursue highly efficient video coding and to maintain good rate-distortion performances of video compression. In this dissertation, several content-adaptive models are developed to increase the speed of video encoding and to achieve better rate/quality control in H.264/AVC. The dissertation consists of three major parts. First, an efficient intra-prediction mode decision mechanism is presented. A projection-based approach, which utilizes the reconstructed surrounding pixels and block content to compute the predicted block residuals (PBR), can effectively eliminate the less probable modes from the computation of Rate Distortion Optimization. According to the PBR and coding information acquired during the encoding process, some prediction modes and macroblock types can be further skipped to accelerate the intra coding. Then, after considering the efficiency of intra coding, we research the issue of Rate-Quantization (R-Q) in the intra coding of H.264/AVC. Assigning an appropriate Quantization Parameter (QP) to the intra-coded frames is very important to the video coding. A content-adaptive R-Q model is thus presented to predict the bit usage of intra-coded frames. The relationship between the QP of a macroblock and the block complexity is derived so that a suitable QP can be determined under a target bit-rate. Since the proposed model is built on macroblocks, Region of Interest (ROI) coding can also be achieved. By adjusting the QP value at the macroblock level, more bits can be assigned to the ROI to better preserve its perceptual quality. Finally, we tackle the problem of rate/quality control for regular video encoding by estimating the resultant quality or distortion associated with QP. A Distortion-Quantization (D-Q) model is proposed to predict the distortion level, which is defined as the difference between the original video frame and the decoded one in the sum of squared errors. As in the R-Q model, the proposed D-Q model also has only one adjustable parameter related to the macroblock content and provides a mapping between QP and the corresponding distortion before the exact encoding process. Given a targeted frame quality measured in peak signal to noise ratio (PSNR), this model helps to assign a suitable QP value to each frame. Two applications are then considered, i.e., the single-pass constant frame PSNR coding and the two-pass coding with the additional bitrate or storage constraint, both of which can facilitate such applications of video archiving and editing.

    摘要..................................................................................................................... i Abstract ............................................................................................................... iii Acknowledgements............................................................................................. v Contents .............................................................................................................. vii List of Figures ..................................................................................................... x List of Tables....................................................................................................... xiii 1. Introduction.................................................................................................. 1 1.1 Significance of the Research . . . . . . . . . . . . . . . . . . 1 1.2 Contributions of the Research . . . . . . . . . . . . . . . . . 4 1.2.1 Projection-Based Fast Intra Coding Algorithm . . . . . . . . 5 1.2.2 Content-Adaptive Rate-Quantization Model and Region-of- Interest Intra Coding . . . . . . . . . . . . . . . . . . . . . . 6 1.2.3 Content-Adaptive Distortion-Quantization Model and Its Application of Constant Quality Video Coding . . . . . . . . . . 7 1.3 Organization of the Dissertation . . . . . . . . . . . . . . . . 8 2. Projection-Based Fast Intra Coding Algorithm ........................................... 10 2.1 Literature Review . . . . . . . . . . . . . . . . . . . . . . . 10 2.1.1 Reducing the Number of Considered Prediction Modes . . . . 10 2.1.2 MB Type Classification . . . . . . . . . . . . . . . . . . . . 12 2.1.3 Simplifying The RDO Evaluation . . . . . . . . . . . . . . . 12 2.2 The Overview of Intra Prediction in H.264/AVC . . . . . . . 13 2.3 Projection-Based Fast Intra Prediction Scheme . . . . . . . . 16 2.3.1 Mode Selection for Intra4  4 . . . . . . . . . . . . . . . . 17 2.3.2 Mode Selection for Intra8  8 . . . . . . . . . . . . . . . . 22 2.3.3 Mode Selection for Intra16  16 and IntraC8  8 . . . . . 27 2.4 Statistical Regression Models for Further Acceleration . . . . 29 2.4.1 Refining the Mode Selection . . . . . . . . . . . . . . . . . . 29 2.4.2 Macroblock Type Skipping . . . . . . . . . . . . . . . . . . 32 2.5 Complete Procedure . . . . . . . . . . . . . . . . . . . . . . 35 2.6 Experimental Results . . . . . . . . . . . . . . . . . . . . . 37 2.7 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 3. Content-Adaptive Rate-Quantization Model and Region of Interest Intra Coding................................................................................................................. 45 3.1 Literature Review . . . . . . . . . . . . . . . . . . . . . . . 45 3.2 Content-Adaptive Rate-Quantization Model . . . . . . . . . . 48 3.3 Region of Interest Coding . . . . . . . . . . . . . . . . . . . 53 3.4 Experimental Results . . . . . . . . . . . . . . . . . . . . . 56 3.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60 4. Content-Adaptive Distortion-Quantization Model and Its Application of Constant Quality Video Coding.......................................................................... 62 4.1 Literature Review . . . . . . . . . . . . . . . . . . . . . . . 62 4.1.1 Rate Control and Quality Control . . . . . . . . . . . . . . . 62 4.1.2 Distortion-Quantization Models . . . . . . . . . . . . . . . . 64 4.1.3 Constant Quality Video Coding . . . . . . . . . . . . . . . . 65 4.2 Concepts of Rate Control . . . . . . . . . . . . . . . . . . . 66 4.3 Content-Adaptive Distortion-Quantization Model . . . . . . . 68 4.3.1 Single-Parameter Distortion-Quantization Model . . . . . . . 68 4.3.2 Estimating the Model Parameter by Content-Adaptive Features 73 4.4 Single-Pass Constant Quality Control . . . . . . . . . . . . . 75 4.4.1 I-Frame Assignment based on Scene Change Detection . . . . 76 4.4.2 D-Q Model Updating . . . . . . . . . . . . . . . . . . . . . 78 4.4.3 The Procedure of Single-Pass Quality Control . . . . . . . . . 81 4.5 Two-Pass Rate Control for Constant PSNR Coding . . . . . . 82 4.5.1 Theoretical Analysis . . . . . . . . . . . . . . . . . . . . . . 83 4.5.2 First-Pass Encoding . . . . . . . . . . . . . . . . . . . . . . 84 4.5.3 Target Distortion Determination . . . . . . . . . . . . . . . . 85 4.5.4 Bit Allocation . . . . . . . . . . . . . . . . . . . . . . . . . 85 4.5.5 Estimating the Model Parameter and D-Q Relation . . . . . . 86 4.5.6 QP Determination . . . . . . . . . . . . . . . . . . . . . . . 87 4.6 Experimental Results . . . . . . . . . . . . . . . . . . . . . 88 4.7 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97 5. Conclusion and Future Work ....................................................................... 99 5.1 Concluding Remarks . . . . . . . . . . . . . . . . . . . . . . 99 5.2 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . 101 Reference ............................................................................................................ 104

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