HEVC低複雜度畫面內預測之研究

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姓名

陳昶毅(Chang-Yi Chen) 查詢紙本館藏

畢業系所

通訊工程學系

論文名稱

HEVC低複雜度畫面內預測之研究
(Low-Complexity Intra Prediction for HEVC)

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摘要(中)

隨著科技的進步，為了因應高畫質(HD)及超高畫質(Ultra HD)的影像需求，一個新的視訊編碼標準稱為高效率視訊編碼(High Efficiency Video Coding,HEVC)被訂定出來，以達到更好的編碼效能。HEVC的編碼單元(Coding Unit , CU)採用了四分樹(Quad-Tree)的編碼架構增加其編碼效能，在畫面內預測中，為了使預測更精準，使用了35個模式來進行預測，同時也大幅的增加其編碼的複雜度。因此本篇論文提出一低複雜度畫面內預測演算法，利用低頻交流值(Low-frequency AC value)及變異數(Variance)影像特徵值於編碼單元決策其深度的切割；於畫面內預測中，藉由變異數及梯度邊界特徵來判斷畫面同質性及預測單元(Prediction Unit,PU)的方向，減少約略模式決策(Rough Mode Decision, RMD)及RDO(Rate-Distortion Optimization)的候選模式個數。由實驗結果可知，我們提出的演算法在維持一定影像品質之下，平均能夠節省42%的整體編碼時間。

摘要(英)

Along with the progress of science and technology, a new video coding standard called HEVC(High Efficiency Video Coding) is developed in order to achieve better coding performance for the emerging HD and Ultra HD video content. The coding unit(CU) of HEVC uses a Quadtree-based coding structure to increase coding performance. In order to predict more accurately, using 35 prediction modes in intra prediction. Simultaneously, it also increases its coding complexity. Hence, in this thesis, we proposed a low-complexity Intra prediction algorithm.We determinate the split of the depth of CU by using low-frequency AC value and Variance. In intra prediction , we use Variance and gradient boundary feature to distinguish the homogeneousness of image and direction of PU(Prediction Unit). Then, reducing the candidates of rough mode decision and rate-distortion optimization. The experimental results show that our proposed algorithm achieves 42% encoding time saving on average without significant degradation of coding performance.

關鍵字(中)

★ 高效率視訊壓縮編碼
★ 編碼單元
★ 畫面內預測
★ 低頻交流值
★ 變異數
★ 梯度法

關鍵字(英)

★ HEVC
★ Coding Unit
★ Intra Prediction
★ Low-frequency AC value
★ Variance
★ Gradient

論文目次

第一章緒論 1
1.1 HEVC視訊壓縮標準簡介 1
1.1.1 HEVC編碼架構簡介 2
1.2 研究動機與目的 6
1.3 論文架構 6
第二章低頻交流值於畫面內快速編碼單元深度決策之研究與探討 7
2.1 畫面內預測 7
2.2 編碼單元深度決策演算法相關文獻回顧 14
2.3 改良型低頻交流值之HEVC畫面內快速編碼單元決策演算
法 19
2.3.1 改良型低頻交流值演算法於影像之特徵及探討 19
2.3.2 效能分析 25
第三章整合型畫面內預測快速編碼單元深度決策之研究與探討 37
3.1基於變異數之HEVC畫面內快速編碼單元深度決策演
算法 37
3.1.1變異數介紹 37
3.1.2變異數用於影像之特徵及探討 38
3.1.3 效能分析 43
3.2整合低頻交流值及變異數之畫面內快速編碼單元決策
演算法 44
3.2.1 整合型畫面內之低頻交流標準差深度決策演算法 45
3.2.2 整合效能分析 46
第四章快速畫面內預測模式決策演算法之研究與探討 56
4.1快速畫面內預測模式決策演算法相關文獻回顧 56
4.2改良型降取樣梯度邊界特徵之快速畫面內模式決策演算法 64
4.2.1 分階降取樣梯度邊界特徵演算法之探討 64
4.2.2 效能分析 71
4.3 結合畫面內預測編碼單元深度決策及模式決策之研究 81
4.3.1 結合畫面內預測編碼單元深度決策及模式決策之快速演
算法 81
4.3.2 合併之總效能分析 82
第五章結論與未來展望 94
參考文獻 95

參考文獻

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[17] G. Bjontegarrd, “Calculation of average PSNR differences between RD curves,“ in ITU-T SC16/Q6 13th VCEG meeting, No.VCEG-M33, Austin, TX, Apr. 2001.
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指導教授

林銀議(Yin-yi Lin)

審核日期

2014-7-22

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