臺灣博碩士論文加值系統

動態估測是在視訊壓縮中很重要的一個議題。在過去的動態估測研究皆基於中心基準策略和最小均方誤差趨勢兩種理論基礎，但是這些方法估測效果不好或著會找到區域性的最小值而非全域性最小值；另外一些方法會依據動態向量估測出啟始位置，這些方法速度快但是結果常常不正確。本篇論文的第一部分敘述了之前的方法容易估測效果不好、找到區域性最小值以及結果不正確的缺失。而提出的多向搜尋趨勢 (Multiple Searching Trend: MST) 方法減少了無效率的搜尋和找到區域性最小值的機會，適應性擴張搜尋區域 (Adaptive Dilated Searching Field: ADSF) 則避免了一開始找到錯誤的起始位置的缺點。將以上 MST 和 ADSF 應用在之前提出的動態估測方法將可得到更快而正確的結果。因此本方法被稱之為雞尾酒式搜尋法 (CockTail Search: CTS)。
在本論文的另一部份提出了一個新的估測標準用來尋找在參考畫面的搜尋視窗中跟現在要被估測的最接近而適合的參考區塊。現金要被壓縮的方塊和其參考方塊的畫素差異值之預測誤差 (Prediction Error: PE) 被拿來利用作為比均方誤差效果更好的選擇最適於做視訊壓縮的區塊估測方法。而在本論文中將PE和MSE合併之估測標準能得到比PE更好之效果。此兩種估測標準應用到UCBDS[4]來比較，結果發現UCBDS和本論文提出之標準合併之法比起UCBDS和MSE合併之法壓縮後檔案容量大小縮減了將近40%。

Motion estimation is the key issue in video compressing. Several methods for motion estimation based on the center biased strategy and minimum mean square error trend searching have been proposed, such as TSS, FSS, UCBDS and MIBAS, but these methods yield poor estimates or find local minima. Many other methods predict the starting point for the estimation, these can be fast but are inaccurate. This study addresses the causes of wrong estimates, local minima and incorrect predictions in the prior estimation methods. The Multiple Searching Trend (MST) is proposed to overcome the problems of ineffective searches and local minima, and the Adaptive Dilated Searching Field (ADSF) is described to prevent prediction from wrong location. Applying MST and ADSF to the listed estimating methods, such as UCBDS, a fast and accurate can be reached. For this this reason, the method is called CockTail Searching (CTS).
In another proposed method, we try to define the new criteria used to determine a referent macro block within the search window in a referent frame, which matches the estimated current macro block in the current frame, in motion estimation process used in MPEG standard. The Prediction Error(PE) in the Pixel Difference(PD) between the referent macro block and the current macro block is defined to be a new criterion which can get better performance in compressed data length than the Mean Square Error(MSE) used by most of motion estimation methods. The other criterion combined PE and MSE is proposed to get better performance than the PE. Two new criteria is applied to a famous motion estimation method, UCBDS, to show the performance of the new criteria. The evaluation results show that using new criteria in UCBDS can get more 40% reduction in compressed data size than the UCBDS with MSE.

中文摘要……………………………………………………………………………..i
Abstract……………………………………………………………………………...iii
List of Figures………………………………………………………………………..iv
List of Tables……………………………………………………………………….....v
Chapter 1 Introduction………………………………………………………………1
Chapter 2 Motion Estimation for Video Compressing…………….……...……..7
Chapter 3 Motivations….….………...……………………………………………21
3.1 Observation in Existing Motion Estimation Methods………………………21
3.2 The Confusions with Mean Square Error………….……………………..32
Chapter 4 The Proposed Methods…………………………………………………38
4.1 Cocktail Search Algorithm (CTS).…………………………………………38
4.2 Criteria for Motion Estimation to Increase Compression Rate……………..46
4.3 UCBDS-mPEN: a Novel Motion Estimation Method to Obtain Higher Compression Rate………………………………………………………………50
Chapter 5 Simulations and Discussions……………………………………...55
5.1 Evaluating Results of CockTail Search Scheme……………………………55
5.2 Evaluating Results of UCBDS-mPEN……………………………………...68
Chapter 6 Conclusions………………..…………………………………………...72
Reference…………………………………………………………………………….73

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