臺灣博碩士論文加值系統

近幾年來隨著無線通訊伴隨變動頻寬等多媒體影像的技術上突破，在H.264/AVC基礎上，推動一個發展中標準可調式視訊編碼(scalable video coding, SVC)，與H.264/AVC相比，多了時間可調性、時間可調性及訊雜比可調性三大特色，但也增加了龐大運算複雜度，其中運算複雜度最大部分都是在移動預測(motion estimation, ME)。在時間可調性基底上，本論文提出ME的方法，利用在基礎層經上升取樣後之殘餘量的特性 在增強層中選擇只作本層ME(normal motion estimation, NME)或跨層ME(inter-layer residual motion estimation, ILRME)，已達到減少50%ME軟硬體運算成本，並藉由群組巨集區塊架構(group of macroblock, GOMB)和動態調整搜索範圍(adaptive search range, ASR)的整點ME演算法( integer motion estimation, IME)，結合低記憶體頻寬、低運算複雜度和高畫質支援。與台灣大學電機系陳良基教授於2005年實現的SVC編碼晶片做比較，頻寬節省35%~40%、內部記憶體平均節省85%，而畫質在HD解析度平均掉0.1dB。

In the past few years, wireless communications with varied bandwidth have achieved innovation in the video compression technology and driven a developing standard for scalable video coding(SVC). H.264/AVC scalable extension extends from H.264/AVC for SVC. As comparing to H.264/AVC, H.264/AVC scalable extension adds three features which provide temporal, spatial and signal-to-noise ration (SNR) scalabilities. These not only allow more flexible than H.264/AVC but also cause huge encoding complexity especially for the motion vector (MV) searching. The proposed motion estimation (ME) process is extended from spatial scalability. It utilizes the property of the up-sampled residual in base layer (BL) for normal motion estimation (NME) selection or for the inter-layer residual motion estimation (ILRME) in enhancement layer (EL). The proposed ME process is applied only on NME or ILRME. Consequently, a half of the software computation or the hardware operation was saved. It combines a low memory bandwidth, low complexity, and high quality supporting integer motion estimation (IME) algorithm by group of macroblock (GOMB) and adaptive search range (ASR). Compared to paper submitted from Nation Taiwan University- Electrical Engineering Institute (NTU-EE) in 2005, the proposed architechture can save between 35% and 40% of external memory bandwidth, and up to 85% of internal memory is reduced, as for video quality, the proposed algorithms loss PSNR by 0.1 dB in average on HD (1280×720) encoding.

摘 要 i
Abstract ii
誌 謝 iv
目 錄 v
表 目 錄 vii
圖 目 錄 x
第一章 序論 1
1.1 前言 1
1.2 研究動機 2
1.3 論文架構 2
第二章 SVC之簡介 3
2.1 SVC之應用 4
2.2 SVC之架構 5
2.3 時間可調性 6
2.3.1 Hierarchical B 7
2.3.2 SVC之編碼順序 8
2.4 空間可調性 9
2.4.1 階層間畫面內預測 10
2.4.2 階層間動量預測 11
2.4.3 階層間殘值預測 12
2.5 訊雜比可調性 14
第三章 SVC之快速移動預測演算法 15
3.1 EL之二種移動預測 16
3.2 動態移動平均值與中間值 20
3.3 偏移殖 26
3.4 SVC之快速移動預測演算法流程 27
第四章 SVC之適用於硬體資料共用與動態調整搜尋範圍之整數點移動預測演算法 32
4.1 群組化之巨集區塊 35
4.1.1 群組化之巨集區塊 35
4.1.2 固定參考視窗演算法 38
4.1.3 PMV偏移共用搜索視窗 42
4.2 動態調整搜索範圍 45
4.2.1 重找PMV機制 45
4.2.2 動態調整搜索範圍 49
4.3 向下取樣搜索演算法 52
4.3.1 向下取樣搜索模式兩步驟搜索 52
4.3.2 所有模式經兩步驟搜索 54
4.3.3 模式16×16經兩步驟搜索 57
第五章 SVC ME演算法之硬體頻寬及內部記憶體分析 62
5.1 NTU-EE晶片頻寬分析 63
5.2 快速移動預測演算法結合整數點移動預測演算法之架構分析for SVC 70
第六章 結論與未來展望 77
6.1 結論 77
6.2 未來展望 77
參考文獻 78

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