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研究生:盧彥亨
研究生(外文):Yan-Heng Lu
論文名稱:基於 3D-HEVC 的立體視訊快速編碼法
論文名稱(外文):Fast Encoding of 3D Color-Plus-Depth Video Based on 3D-HEVC
指導教授:賴文能賴文能引用關係
指導教授(外文):Wen Neng Lie
口試委員:蕭旭峰林鼎然賴文能江瑞秋
口試委員(外文):Hsu-Feng HsiaoTing-Lan LinWen-Neng LieJui-Chiu Chiang
口試日期:2014-09-26
學位類別:碩士
校院名稱:國立中正大學
系所名稱:電機工程研究所
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2014
畢業學年度:103
語文別:中文
論文頁數:59
中文關鍵詞:立體視訊編碼
外文關鍵詞:3DHEVC
相關次數:
  • 被引用被引用:1
  • 點閱點閱:728
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  • 下載下載:64
  • 收藏至我的研究室書目清單書目收藏:0
為了降低 3D 多視角視訊的編碼複雜度,本文在 3D-HEVC 的架構下提出一個基於特徵向量比對與類神經網路訓練器的影像編碼快速決策演算法。有別於以往影像編碼的加速方法只利用單方面的特徵 (彩色編碼利用彩色影像特徵),本文的演算法結合彩色與深度影像特徵互相協助彩色與深度影像的編碼。在彩色編碼的部分,我們擷取影像間的光流向量,並且加入深度影像邊緣資訊結合成特徵向量。透過比對當下編碼區塊中的特徵向量與歷史編碼區塊所相對應的特徵向量,以 kNN 的比對法來加速 CU 的切割決策。為了使快速決策的結果更加貼近 HEVC 原有RDO 所得到的編碼切割決策,我們提出前處理的演算法,將畫面中所有光流向量扣除全域移動向量求得物體真實的移動向量,使得特徵向量比對後的快速編碼決策結果更為正確。在深度編碼的部分,我們以彩色影像間的光流向量加上深度影像特徵作為類神經網路分類器的輸入。在編碼過程中,以離線訓練後的分類器可線上執行編碼區塊切割的快速決策。相對於原始的 3D-HEVC 編碼程式,本文所提出的彩色編碼演算法平均可節省 46.57% 的編碼時間,編碼位元率的提升僅有 0.4%,而 PSNR 微幅下降 0.04 dB。另一方面,深度編碼演算法平均節省 35.79% 的編碼時間,編碼位元率反而下降 2.65%,而 PSNR 則下降 0.16 dB。相較於其他文獻的實驗結果,本文的演算法不僅節省較多編碼時間,編碼後重建的影像品質也較好。

目錄 i
圖目錄 iii
表目錄 v
第一章 緒論 1
1.1 研究背景與動機 1
1.2 相關研究 2
1.3 論文架構 6
第二章 HEVC 編碼技術 7
2.1 2D-HEVC編碼架構 7
2.1.1 編碼單位 9
2.1.2 階層式編碼架構 11
2.1.3 畫面間預測 14
2.1.4 畫面內預測 16
2.2 3D-HEVC編碼架構 18
2.2.1 彩色影像編碼工具 19
2.2.2 深度影像編碼工具 21
第三章 快速3D-HEVC編碼演算法 25
3.1 彩色影像編碼快速演算法 26
3.1.1 基於光流分析之編碼區塊切割快速決策 27
3.1.2 基於全域移動向量補償之編碼預測模式快速決策 30
3.2 深度影像編碼快速演算法 36
3.2.1 類神經網路 37
3.2.2 編碼區塊之影像特徵擷取 38
3.2.3 編碼區塊切割之快速決策 39
第四章 實驗結果與討論 41
4.1 實驗環境 41
4.2 實驗結果與分析 42
第五章 結論與未來工作 55
參考文獻 57

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