影像透過壓縮的技術在網路上傳輸時，雖然減少了大量傳輸的資料量，但若有部分封包遺失、損毀或被雜訊干擾，將造成部分影像無法重建而產生一連串失真的影像， 此時需要錯誤回復(error resilience)與錯誤隱藏(error concealment)去維護影像的品質。
本篇論文著重於改善錯誤隱藏回復連續區塊(macroblock)的錯誤。錯誤的區塊會參照相鄰正確的區塊或已修復的區塊，若一個區塊修復的結果很差，將造成相鄰錯誤的區塊參照此區塊修復時，會參照到錯誤的資訊，導致錯誤傳遞(error propagation)，所以錯誤區塊修復的順序與參照從錯誤中修復的區塊的信任度將會影響修復的品質。
於2009年時，Qian等人提出了一個針對連續錯誤區塊的修復順序決定方法。該演算法藉由分析相鄰區塊的紋理複雜性，決定其修復的優先順序。這是因為Qian等人認為具有較多特徵點的高紋理複雜區塊，能提供其他錯誤區塊較佳的參考性，因此應最先被修復；反之，較低紋理的錯誤區塊因為不具有決定性的特徵，應給予較後的修復次序。然而，我們觀察到若只依紋理性的強弱決定修復的順序，仍然有很大的機會修復出不正確的區塊內容，導致錯誤傳遞的情形產生。為了改善錯誤傳遞的狀況，我們的演算法計算了區塊的信任度並使用了兩個等待佇列來控制整個錯誤修復的流程。一個高信任度的損壞區塊集代表它有較高的可能性獲得較佳的回復結果，應該優先進行回復。在修復該區塊之後，我們估計該區塊的回復品質，若估計的品質並沒有反映出它的信任度，該區塊將會被放入第一個等待佇列內，以避免其他區塊修補時參照到錯誤的資訊。第二個等待佇列中則是存放紋理複雜度較低的區塊，這類區塊由於可參考的特徵太少，導致修復的結果並不理想。因此，它們將被放入到第二個等待佇列中，待第一個佇列內的錯誤區塊都修復完成後，才會被處理。另外，當每個區塊被修復之後，我們會動態的更新周邊區塊的信任度，來獲得到最準確的資訊，使得修復的影片能具有最佳的品質。從六個標準測試影片的實驗結果顯示，我們的錯誤修復方法與近幾年的五個研究方法相較之下，可達到最佳的影片修復品質。

Error concealment plays an important role in robust transmission. Recently, Qian et al. presented an efficient video error concealment algorithm for connected erroneous regions in video sequences. For solving the error propagation problem existing in Qian et al.'s algorithm, with the help of two waiting pools, we propose an adaptive priority strategy to determine better order for executing error concealment. Based on eight video sequences, our proposed algorithm is compared with five existing error concealment algorithms. Experimental results demonstrate our algorithm received the best error concealment results in terms of quality.

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