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研究生： 蔡安朝
Tsai, An-Chao
論文名稱： H.264/AVC視訊編碼器之快速演算法與SVC內插演算法設計
Fast Algorithms for Intra Prediction in H.264/AVC Encoder and Image Scaling Algorithm for SVC
指導教授： 王駿發
Wang, Jhing-Fa
學位類別： 博士
Doctor
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： 2010
畢業學年度： 98
語文別： 英文
論文頁數： 99
中文關鍵詞： 內框編碼 、邊緣方向偵測 、快速演算法 、多區塊移動估計 、空間可調性編碼 、內插
外文關鍵詞： H.264/AVC, intra prediction, edge detection, fast algorithm, H.264/SVC, spatial scalability, up-sampling
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    • H.264為最新的數位視訊壓縮標準，藉由引進不同的編碼特點來獲得更高的壓縮效率。 其中，內框模式預測是由編碼最佳化所獲得，這表示編碼器必需計算所有的內框模式預測的編碼組合。然而這個最佳化的計算過程對於即時應用卻是一大挑戰。本論文主要針對內框模式預測提出加速編碼速度之快速演算法。此外對於H.264/SVC可調性視訊編碼我們也觀察到其所使用的內插演算法將直接影響編碼的效率，所以我們也著重於研究並發展能增加其效能的內插演算法。
    首先，我們提出利用梯度來選擇適當的編碼模式，使用計算出的方向特徵去選擇合適的模式進而減少最佳化運算的時間。實驗結果顯示所提出的演算可節省約76%的內框預測編碼時間，伴隨著微小的PSNR損失。其次，我們利用計算次區塊與點對點的資訊進一步提出兩個快速、有效且可靠度高的方向偵測演算法。兩者演算法皆可有效預測出在區塊內的方向，成功減少最佳化編碼計算的時間。由結果可知兩者演算法可減少約60%的編碼時間。我們也把所提出的次區塊模式選擇演算法進ㄧ步實現成硬體。一個內框模式預測快速的模式選擇硬體設計，使用0.18 μm 的製程實現，面積為0.12×0.12 mm2。
    最後，針對H.264可調性視訊編碼中之空間可調性編碼提出一分類式多濾波器內插演算法。在影像中把紋理區塊與非紋理區塊做分類，對於紋理區塊我們利用Wiener濾波器，而非紋理區塊則使用一般的濾波器來內插影像，此方法可有效保留紋理於內插完仍清楚呈現在影像中。與標準碼之方法相比實驗結果顯示所提出的方法有效提升PSNR約0.5dB且降低約9%的位元率。

    H.264, MPEG-4 Part 10, is the latest digital video coding standard that achieves very high data compression by using several new coding features. In H.264/AVC intra frame coding, the rate-distortion optimization (RDO) is employed to select the optimal coding mode to achieve the minimum rate-distortion cost. Due to a large number of combinations of coding modes, the computational burden of intra prediction becomes extremely high for real time applications. This dissertation aims to propose fast algorithms for intra coding to enhance the coding speed while retain the coding performance. The up-sampling method employed in H.264/SVC is directly proportional to the video quality. In order to improve the video quality with an effective up-sampling method, we also propose the up-sampling algorithm for H.264/SVC which greatly reduces the bit-rate and increases the PSNR.
    We first present an intensity gradient approach for intra prediction, which enhances the performance and efficiency of the encoder. The orientation features are utilized to select a subset of prediction modes to be involved in the rate-distortion calculation so that the encoding time can be reduced. The proposed algorithm introduces slight PSNR degradation and bit-rate increase but saves around 76% of the total encoding time with all intra frame coding. We also proposed two fast, efficient but reliable direction detection algorithms by computing subblock and pixel direction differences for fast intra mode decision. Both proposed methods effectively estimate the edge direction inside the block to narrow down the predictive modes to reduce the RDO computation. Experimental results show that the proposed methods can reduce the encoding time by about 60% with negligible loss of coding performance. For hardware realization, a fast mode decision VLSI circuit for intra prediction with the silicon core size of 0.12×0.12 mm2 at 0.18 m CMOS technology is implemented.
    For H.264/SVC up-sampling process, we propose a classified multi-filter up-sampling algorithm (CMFUSA) in spatial scalability which classifies an image region as edges and non edges. An appropriate filter is then applied to up-sample the image. In the proposed scheme, we applied the Wiener filter for edges and conventional filter for non edges within the defined window size to preserve the edges in the up sampled image sequence. The experimental results show that the average PSNR improvement and bit-rate reduction are 0.5 dB and 9%, respectively, which confirm that the performance of the proposed method is better than that of the existing H.264/Scalable Video Coding standard.

    Abstract i 誌 謝 v List of Tables x List of Figures xii Chapter 1 Introduction 15 1.1 Motivation 15 1.1.1 Intra coding in AVC 15 1.1.2 Spatial scalability in SVC 15 1.2 Main Contribution 16 1.3 Dissertation Outline 17 Chapter 2 Basic Concepts of the H.264/AVC Intra Prediction and Spatial Scalability of SVC 18 2.1 Overview of H.264/AVC Standard 18 2.2 Intra Prediction 19 2.2.1 4×4 Luma Prediction Modes 20 2.2.2 16×16 Luma Prediction Modes 22 2.2.3 8×8 Chroma Prediction Modes 23 2.2.4 Mode Decision 23 2.3 Basic concept of Scalable Video Coding 26 2.4 Literature Review 27 2.4.1 Previous Work in Intra 27 2.4.2 Previous Work in Spatial Salability of SVC 28 Chapter 3 Intensity Gradient Technique for Efficient Intra Prediction in H.264/AVC 30 3.1 Introduction 30 3.2 Proposed Fast Intra Mode Decision Algorithm 30 3.2.1 Intensity Gradient Filter in Intra Prediction 31 3.2.2 Intra luma 4×4 prediction 32 3.2.2.1 Vertical 32 3.2.2.2 Horizontal 33 3.2.2.3 Diagonal Down-Left 33 3.2.2.4 Diagonal Down-Right 34 3.2.2.5 Remaining Modes 34 3.2.3 Intra luma 8×8 prediction 35 3.2.4 Intra 16×16 luma and 8×8 chroma prediction modes 36 3.3 Experimental Results 37 3.4 Summary 43 Chapter 4 Proposed Effective Subblock-based and Pixel-based Fast Direction Detections for H.264 Intra Prediction 45 4.1 Introduction 45 4.2 Proposed Pixel-based Direction Detection (PDD) Method 46 4.2.1 Intra Luma 4×4 Prediction 46 4.2.2 Intra Luma 16×16 and Chroma 8×8 Prediction 48 4.3 Proposed Subblock-based Direction Detection (SDD) Method 49 4.3.1 Intra Luma 4×4 Prediction 49 4.3.2 Intra Luma 16×16 and Chroma 8×8 Prediction 51 4.4 Experimental Results and Discussions 52 4.5 Hardware Architecture of SDD Method 58 4.5.1 Block partition 58 4.5.2 Direction Error Strength Computation 59 4.5.3 Edge direction decision 60 4.6 Summary 61 Chapter 5 Classified Multi-Filter Up-Sampling Algorithm in Spatial Scalability for H.264/SVC Encoder 62 5.1 Introduction 62 5.2 Proposed Classified Multi Filter Up-Sample Algorithm for Spatial Scalability (CMFUSA) 63 5.2.1 Wiener Filter 64 5.2.2 Proposed CMFUSA Algorithm 66 5.2.3 Diagonal up-sampling 68 5.2.4 Crisscross up-sampling 70 5.3 Experimental Results and Discussion 72 5.3.1 Performance Analysis 73 5.3.2 Experiments for IPPP test 81 5.3.3 Experiments for non-dyadic up-sampling 84 5.4 Summary 87 Chapter 6 Conclusion and Future Work 88 6.1 Conclusion 88 6.2 Future Work 89 References 90 Publication List 97

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