以時空相關性做移動估測輔助之解交錯演算法

Abstract

解交錯演算法是用來將交錯式的影像轉換成循序式的掃瞄格式。移動可適性的演算法提供了可接受的品質唯其在移動區域的畫質仍有待提升。在各種不同的解交錯技術中，利用移動補償的解交錯演算法在所估計移動資訊可靠的話可以提供最佳的性能，然而它容易受到不正確的移動估計和薄弱的錯誤保護機制因而降低了畫面的品質。本篇論文提出一個具有高度正確性的移動估計和可靠的錯誤偵測的移動補償解交錯演算法。為了得到更精確的移動資訊，本文提出了一個以時空相關性為輔助的移動估計演算法，利用了鄰近的移動向量在時空中的相關性來還原出物體真實的移動向量。為了要排除錯誤的時間資訊，我們提出一個階層性的移動資訊驗證，可以有效的偵測在大小範圍可能出現的錯誤。由實驗的模擬結果顯示所提出的演算法性能較其他演算法優越並且在各種不同的測試檔中都能提供高品質的輸出。

De-interlacing algorithms are used to convert interlaced video into progressive scanning format. The motion adaptive technique provides acceptable picture quality, but the quality of the motion area still needs to be improved. Among the various de-interlacing techniques, the motion compensated de-interlacing technique provides the best performance if the estimated motion information is reliable. However, it suffers from inaccurate motion estimation, and the weak error protection thus deteriorates the visual quality. This thesis presents a motion compensated de-interlacing algorithm with highly accurate motion estimation and robust error detection. In order to obtain more accurate motion information, spatial-temporal correlation assisted motion estimation is proposed. The spatial and temporal correlations among the motion vectors are exploited to find the true motion of the object. In order to reject incorrect temporal information, a hierarchical MV reliability verification is provided. The possible defects in both large and small areas can be detected effectively. The experimental results show that the proposed algorithm outperforms existing algorithms and produces high quality de-interlaced results in various video sequences.