本文提出了一種快速有效的新型區域深度圖放大方法及其在無位置圖之可逆 資料隱藏應用。在所提出的深度圖放大中，首先，將所有缺失的深度像素劃分為 三個不相交的區域：平滑區域，半平滑區域和非平滑區域。然後，我們提出深度 複製，平均值和雙三次插值方法，分別快速重建平滑，半平滑和非平滑缺失深度 像素。此外，根據每個缺失深度像素的相鄰原始深度像素的特殊約束，我們提出 了一種有效的結合深度圖放大和無位置圖之可逆資料隱藏(JUR) 方法。實驗結果 顯示我們的深度圖放大方法相對於最先進的深度圖放大方法具有執行時間以及品 質優勢。同時與最先進的資料隱藏方法相比，我們的JUR 方法具有更多藏入量和 以及更好的品質優勢。

In this thesis, we propose a fast and effective novel region-based depth map upsampling method and its application to the location map-free reversible data hiding. In the proposed upsampling method, first, all the missing depth pixels are partitioned into three disjoint regions: the homogeneous, semi-homogeneous, and non-homogeneous regions. Then, we propose the depth copying, mean value, and bicubic interpolation approaches to reconstruct the homogeneous, semi-homogeneous, and non-homogeneous missing depth pixels quickly, respectively. Furthermore, according to the special constraint on the neighboring true depth pixels of each missing depth pixel, we propose an effective joint depth map upsampling and location map-free reversible data hiding method, called the JUR method. Based on the typical test depth maps, the comprehensive experiments have been carried out to not only justify the execution-time and quality merits of the upsampled depth maps by our upsampling method relative to the state-of-the-art methods, but also to justify the embedding capacity and quality merits of our JUR method when compared with the stateof- the-art methods.

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