臺灣博碩士論文加值系統

High Efficiency Video Coding (HEVC) 是最新一代的視訊壓縮標準。和前幾代標準相較之下,它提供更佳的壓縮效能。HEVC 採取了四元樹結構，它允許以Coding Unit Tree (CTU) 為起點進行遞迴編碼。四元樹結構是導致壓縮效能提升的一大因素，但同時也提升了運算複雜度。

為了降低運算複雜度，於是我們提出了一個快速編碼單元決策演算法。此決策的演算法是由彈性的深度範圍和提早裁剪的測試所組成。首先，針對一塊CTU 編碼時，我們用彈性的深度範圍而不是固定的深度範圍。接著，針對每塊Coding Unit (CU)，我們會執行早期裁剪的測試，此測試是根據貝氏定理所制定而出。

相較於HEVC test model 12.0 (HM 12.0)，從實驗結果得知，我們所提出的演算法平均可以節省60.11%的編碼時間，同時會增加2.4%的位元率和0.1dB的亮度PSNR失真。

High Efficiency Video Coding (HEVC) is the newest video coding standard. It provides the better compression performance compared with the existing standards. HEVC adopts the quad-tree structure which allows recursive splitting into four equally sized nodes, starting from the Coding Tree Unit (CTU). The quad-tree structure causes the better compression efficiency, but it requires the higher computational complexity.

In order to reduce the computational complexity, we propose a fast CU size decision algorithm. The proposed algorithm consists of adaptive depth range and early pruning test. First, we use adaptive depth range instead of fixed depth range for a CTU encoding. Then, for each CU, the early pruning test is performed at each depth level according to Bayes rule based on the full RD costs.

Compared with the HEVC test model 12.0 (HM 12.0), experimental results show the proposed method achieves the reduction of encoding time by 60.11%, the increment of bitrate by 2.4%, and 0.1 dB Y-PSNR loss, on average.

摘要..I
Abstract..II
誌謝..IV
CONTENTS..V
LIST OF TABLES..VII
LIST OF FIGURES..X
Chapter 1 Introduction..1
1.1 HEVC Video Compression Standard..1
1.2 Motivation..2
1.3 Thesis Organization..3
Chapter 2 Background..4
2.1 Overview of HM Encoder [2] [4]..4
2.2 Picture Partitioning [2] [4]..5
2.2.1 CTU Partitioning..5
2.2.2 Coding Unit (CU)..5
2.2.3 Prediction Unit (PU)..6
2.2.4 Transform Unit (TU)..7
2.3 Prediction Mode [4] [6]..7
2.3.1 Intra Prediction..7
2.3.2 Inter Prediction..10
2.4 Transform and Quantization [4] [7]..11
2.5 Entropy Coding [4] [8]..11
2.6 In-loop Filter..11
2.6.1 Deblocking Filter [4] [9]..11
2.6.2 Sample Adaptive Offset (SAO) Filter [4] [10]..12
Chapter 3 Related Works..15
Chapter 4 Proposed Method..20
4.1 Proposed Method Concept..20
4.2 Adaptive Depth Range..20
4.3 Early Pruning Test..28
4.4 Overall Algorithm..39
Chapter 5 Experimental Results..41
5.1 Overview..41
5.2 Experimental Configuration..41
5.3 Measuring Criteria..49
5.3.1 Peak Signal to Noise Ratio (PSNR)..49
5.3.2 Bitrate..49
5.3.3 Calculation Equation..49
5.4 Experimental Results..50
Chapter 6 Conclusions..81
Reference..82

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