本篇論文提出兩種利用顯著特點來增強視覺效果呈現的方法。首先提出在視訊影像調整時能維護感興趣區域原始長寬比的方法，並且應用於可調式視訊編碼標準，可以快速且有效率地決定視訊影像中運動物體為感興趣區域。實驗結果可證明我們所提出的方法不僅允許任意調整視訊影像大小，而且還可以保持重要顯著特點的原始比例，讓觀看者對於感興趣區塊有更好的品質。影像解碼時，小幅度的時間增加可以證明我們所提出的架構具有低計算複雜度的特性。接下來我們也提出了一種多視角影像及深度影像壓縮的方法，包含快速決定將使用的畫框內預測模式和多視角影像的預測及建立。前者是基於圖像內容的物體邊緣方向來進行模式預測；後者使用一個單一視角影像和相對應的深度影像來預測另一視角的影像，取代現今壓縮標準中在預測時所需大量計算來降低編碼時所需時間。

In this thesis, we propose two different algorithms to enhance the visual presentation by prominent features in video coding. First, we proposed a video resizing method for Scalable Video Coding（SVC）to keep the original aspect ratio of region-of-interest（ROI）. The ROI is determined effectively by extracting the moving objects in the video sequence. Experimental results show that our method not only allows arbitrary video resizing, but also maintains the original ratio of important prominent features. Small overhead of decoding time justifies that the proposed framework has low computational complexity. Second, we also present a new method for multiview and depth image compression, including fast intra-prediction mode decision and multiview image reconstruction. The former is based on the direction detection of the objects boundary inside the image. The latter uses one single view and the corresponding depth image to predict the image of the other. The proposed scheme saves a lot of computing in the prediction process to reduce the encoding time.

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