臺灣博碩士論文加值系統

影像編碼的應用範圍非常廣泛，包含高畫質電視、監視器、網路影像傳輸，甚至於需要應用到即時編解碼的視訊或電視直播等。
近年來，H.265/HEVC動態影像壓縮編碼標準在視訊多媒體扮演著相當重要的角色，原因是他能在相同的影像品質下，擁有比以往的編碼標準(H.264/AVC)更佳的壓縮效率。
HEVC能擁有這樣優異的壓縮效率不是沒有代價的，壓縮效率提高代表著計算複雜度的提升，導致影像即時編解碼的實現受到阻礙，在HEVC中又多加入了一濾波器，稱為自適應樣點偏移濾波器(Sample Adaptive Offset Filter)，此濾波器作用為將重建後的畫面失真以樣點為單位做補償，而這種失真我們稱為環失真(Ringing Artifact)，自適應樣點偏移濾波器主要分為兩個種類，分別為EO (Edge Offset)與BO (Band Offset)，但SAO在選擇EO或BO及計算補償值花費許多時間。
本論文提出一種能加速決定EO或BO的演算法，降低SAO的運算時間，最終得到可減少整體編碼平均9%運算量。

Video coding is widely used for many applications, including broadcast of high definition (HD) TV, security applications, internets and mobile network video and Real-Time conversational applications such as video chat and telepresence systems.
In recent years, H.265/HEVC video coding standard played a very important role. The reason is that with the same quality, the HEVC has better compression efficiency than the H.264/AVC.
To have such excellent compression efficiency, HEVC pays cost. The higher compression efficiency represents the higher complexity of computation, resulting in real-time video codec implementations hampered. The HEVC added another filter called Sample Adaptive Offset Filter. This filter is to reduce sample distortion and add the offset to each samples, we call the distortion as Ringing Artifact. The main two type of the SAO are Edge Offset (EO) and Band Offset (BO), but we spend a lot of time to decide the type.
This thesis presents a fast SAO type decision algorithm to reduce the computation time. Finally, the proposed fast SAO decision algorithm can reduce 9% computation time.

Chapter 1 Introduction...1
1.1 Research Background...1
1.2 Motivation...4
1.3 Thesis Organization...6
Chapter 2 Overview the H.265/HEVC...8
2.1 Coding Tree Unit (CTU) of the H.265/HEVC...9
2.2 Coding Tree Block (CTB) of the H.265/HEVC...10
2.3 Coding Unit (CU) and Coding Block (CB) of the H.265/HEVC...11
2.4 Prediction Block of the H.265/HEVC...13
2.5 Transform Block (TB) of the H.265/HEVC...13
2.6 In-loop deblocking filter of the H.265/HEVC...14
2.7 Coding Unit(CU) Quad-tree structure...26
2.8 Sum of Absolute Differences (SAD)...27
2.9 Sample Adaptive Offset (SAO) of the H.265/HEVC ...28
2.10 Edge Offset (EO)...28
2.11 Band Offset (BO)...30
Chapter 3 Proposed Algorithm...33
3.1 Proposed Direction and SAO Type Decision Algorithm...33
3.1.1 Coding Unit (CU) Classification...33
3.1.2 Coding Unit (CU) Direction Detection...37
3.1.3 Sample Classification...40
3.1.4 Sample Offsets...41
3.2 Proposed Fast SAO Algorithm...43
Chapter 4 Experimental Results...44
4.1 Overview of Experimental Results...44
4.2 Experimental Configuration...44
4.3 Measuring Criteria...45
4.3.1 PSNR (Peak Signal-to-Noise Ratio)...45
4.3.2 BD (Bjontegaard Delta)-PSNR...46
4.3.3 Bit Rate...47
4.4 Experimental Results...48
4.4.1 Coding Performance of Proposed Algorithm...48
4.4.1.1 Class A (2560x1600)...49
4.4.1.2 Class B (1920x1080)...50
4.4.1.3 Class C (416x240)...53
4.4.1.4 Class D (832x480)...55
4.4.1.5 Class E (1280x720)...57
Chapter 5 Conclusions...59
REFERENCES...60

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