以線上統計為基礎應用於CGS可調式編碼器之快速模式決策

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姓名

吳柏宗(Bo-Zong Wu) 查詢紙本館藏

畢業系所

通訊工程學系

論文名稱

以線上統計為基礎應用於CGS可調式編碼器之快速模式決策
(Online Statistics Based Fast Mode Decision for Scalable Video Coding with CGS)

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摘要(中)

可調式視訊編碼透過層際間(inter-layer)預測的技術，大幅改善編碼效能，但也大幅提高了計算複雜度。對於不同的視訊影片和編碼參數下，如何降低編碼的複雜度是增加可調式視訊編碼應用性的關鍵。本論文對於CGS可調性提出了一個以線上統計為基礎的快速模式決策演算法，包含為訓練階段和加速階段。在訓練階段，我們線上統計層際間的模式分佈、RD cost increase以及層際內RD cost increase關聯性以確保此統計能適應不同編碼設定下與不同視訊內容，並應用以此設計貝氏分類器和提早終止演算法以達成快速模式決策。在加速階段，依據貝氏分類器之loss functions可減少候選modes的數量並排序候選modes，提早終止演算法則進一步減少測試modes的數量。實驗顯示，我們提出的加速演算法可以節省73%左右的編碼時間，如果只考慮加速階段，則最多可節省78%左右的編碼時間，並且在不同的量化參數(QP)與影片內容時皆下幾乎不會造成PSNR的下降及位元率(bitrate)的上升。

摘要(英)

Duo to the high coding complexity of scalable video coding (SVC) that tests all candidate modes in RD optimization (RDO), in this thesis, we propose an on-line statistics based fast mode decision algorithm for SVC with CGS scalability. This algorithm consists of a training stage and an acceleration stage. At the training stage, the encoder gets the correlations of inter-layer mode distributions and intra and inter-layer RD cost increases. By this, we not only consider the probability distribution functions of optimal modes but also take the RD cost increase that caused by selecting a non-optimal mode into account. At the acceleration stage, the Bayesian classifier and an early termination algorithm are applied to reduce the candidate modes in RDO. By online statistics, the encoder can change the decision rule to reduce the RD performance loss for different sequences and QP sets. The experimental results show that our proposed algorithm can speed up to 73%. At the acceleration stage, our proposed algorithm can speed up to 78% with negligible RD performance loss.

關鍵字(中)

★ 快速模式決策
★ 貝氏分類器
★ 線上統計
★ 可調式視訊編碼

關鍵字(英)

★ fast mode decision
★ Bayesian classifier
★ on-line statistic
★ scalable video coding

論文目次

摘要 i
Abstract ii
目錄 iv
圖目錄 vi
表目錄 viii
第一章緒論 1
1.1 前言 1
1.2 研究動機 2
1.3 研究方法 3
1.4 論文架構 3
第二章 H.264 可調式視訊編碼器介紹 4
2.1 可調式視訊編碼器介紹 4
2.2 可調式視訊編碼架構 5
2.2.1 時間可調性(Temporal Scalability ) 6
2.2.2 空間可調性(Temporal Scalability) 7
2.2.3 雜訊比可調性(SNR Scalability) 8
2.3 基礎層之編碼技術介紹 8
2.4 增進可調式視訊編碼效率之工具 10
2.4.1 層際間畫面內編碼預測(Inter-Layer Intra Prediction) 11
2.4.2 層際間運動向量預測(Inter-Layer Motion Prediction) 12
2.4.3 層際間殘餘資訊預測(Inter-Layer Residual Prediction) 12
2.5 總結 13
第三章可調式視訊編碼器之快速演算法現況 14
3.1快速運動估測演算法 14
3.2快速模式決策演算法 15
3.3 提早終止(Early Termination)模式決策演算法 17
3.4 總結 18
第四章本論文所出之快速巨集區塊模式演算法 19
4.1 以線上統計為基礎之快速巨集區塊模式決策演算法 19
4.2以貝氏分類器為基礎之快速巨集區塊模式決策 20
4.2.1 層際間巨集區塊之關聯性 20
4.2.2 層際內Block Mode分佈關聯性 31
4.2.3 層際內RD Cost Increase關聯性 32
4.2.4 應用貝氏分類器於快速模式決策 34
4.3提早終止巨集區塊模式決策演算法 36
4.3.1 層際間RD cost increase關聯性 36
4.3.2應用層際間RD cost increase於提早終止區塊模式決策演算法 38
第五章實驗結果 43
5.1 實驗環境與參數設定 43
5.2貝氏分類器之快速模式決策演算法 44
5.3結合貝氏分類器與提早終止之快速模式決策演算法 47
5.4 總結 53
第六章結論與未來展望 54
參考文獻 55
Publications 57

參考文獻

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指導教授

唐之瑋(Chih-Wei Tang)

審核日期

2010-7-22

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