適應性動態估測即時影像穩定系統

Abstract

本研究中提出一套適應性動態偵測即時影像穩定系統，目的在縮短系統之執行時間與提供良好的影像穩定品質。在動態偵測方面，我們使用小方塊搜尋方式，並提出用Sobel edge detection方式來增加Global Motion Vector (GMV)之可靠性而且可以減少在計算上所花的時間。而GMV估測則是提出Alpha-trimed mean filter方法，此一方法可以去除一些較異常的移動向量值，提升GMV估測的準確性。在GMV修正方面，提出小波轉換的方法，並比較不同的小波轉換層數與濾波器，提出一個較好的結果來使用。在即時性的考量之下，有關一些資料的搬移方面，記憶體的配置與搬移會影響執行時間的多寡，為了減少時間的花費，本研究將RGB三個平面的資料儲存在同一個記憶體而不分開儲存以便能縮減執行時間，達到即時性的要求。在一般系統常用的影像尺寸352 x 288（QVGA）環境下，系統仍能提供平均每秒約20-30以上的畫面，以提供給系統使用者相當流暢的畫面。最後本系統於WINDOWS平台下以Borland C++ Builder來實現。In this paper, we propose a new image stabilization system by using adaptive motion estimation. Our aims are to decrease the execution time of a stabilized system and obtain a more stable video sequence. The stabilization system contains two parts: motion estimation and motion correction. In the motion estimation, we use a small diamond search and Sobel edge detection to increase the reliability of the prediction of block motion. And we use an alpha-trimmed mean filter to yield a global motion vector (GMV) for each frame. The proposed filter can remove unusual motion vectors and promote the accuracy of GMV estimation. In motion correction, we make use of wavelet transform. After comparing the usages of different decomposition layers and different wavelet functions, we choose two-layer decomposition and the 9/7 filter function as our experimental standard because of its better performance. For real-time applications, the allocation and the movement of storage memory generally influence the execution time of a system. In order to reduce such time consuming, we store the RGB data in a continuous block of memory instead of storing them separately in three blocks of memory. For the image size of 352 x 288 (CIF format), the proposed system can provide about 20-30 frames per second on average and result in a stabilized video sequence for users. Finally, this system is programmed with Borland C++ Builder.