在現今時日下，探討影像傳輸在容易發生錯誤的頻道裡是不可或缺的課題。藉由不同的影像壓縮編碼標準，如H.264和MPEG4等使得影像資訊流的大小可以壓縮到在目前的頻寬裡達到即時撥放的效果。但是，一旦影像資訊封包受到干擾而遺失掉落，那麼會因為壓縮編碼移除多餘資訊和前後連續性來達到高壓縮率的關係，使得空間域的錯誤擴散和時間域的錯誤傳遞，讓影像品質大幅下降。為了提升上述錯誤而下降的影像品質，使用錯誤修補會是一個有利的修正方式。利用壓縮後留下的空間域與時間域的關聯性，解碼端的錯誤修補成為不會對網路頻寬以及編碼端產生影響的一個獨立工作。
這篇論述主要是引用賽局理論來建構一個新的錯誤修補架構，為了要能夠適應各式各樣的影像在空間性上的內容資訊，這裡提供了四種空間域的錯誤修補方法來分別帶入不同的情況使用。其中一種是修改後的多方向性內插修補法，用來在發生錯誤的區塊中如果是具有多種邊緣經過之性質，可以加強其錯誤修補的正確性，藉此達到更多的峰值信噪比（PSNR）。另一種則是修改後的最佳鄰近區塊比對法，他是將發生錯誤的區塊已另一相似區塊給予取代，但搜尋費時，所以修改後的方法是著重在時間的效率性。最後兩種是雙線性內插法跟方向性內插法，綜合以上四種方法在每一個錯誤的區塊中用賽局的方式去建構出一個競爭的局面，藉由比對其空間域上的關聯性資訊來給予不同的喜好值，其喜好值最大者即可贏得此賽局，而贏家就是要拿來作為錯誤修補的方法。

The transmission for the video stream over the error-prone channel is the indispensable subject to discuss in the present days. By the variant video compressed coding standards such as H.264 and MPEG4, the video stream can possess a highly compressed rate to transmit in the real-time display. However, once the packet erasures happened, it can cause serious error propagation during the transmission process. Due to the error-catalyzed situation, the error concealment techniques utilize the remaining spatial and temporal relativity to reconstruct the corrupted data is necessary.
The discussion in this thesis is mainly attempted to introduce a novel error concealment structure constructed by the game theory. In order to adapt the various circumstances of the possible image contents, there are four different Spatial Error Concealment algorithms to provide. One of them is a modified multi-directional interpolation used to enhance the multiple edge erroneous blocks; another one is also a modified best neighborhood matching efficiently for shortening the processing time; the remaining method are Bilinear interpolation and the directional interpolation. These four methods are selected by playing a game theory based structure to make it consider much broader with the variant spatial relativities. The best strategy which possesses the highest payoff value is considered as the suitable algorithm to reconstruct the erroneous block.

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