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| 研究生： |
Vincent Kusuma Vincent Kusuma |
|---|---|
| 論文名稱： |
植基於結合深度合成無誤差模型及藏量最大化之可逆式資料隱藏應用於深度圖 Joint Depth No-Synthesis-Error Model and Embedding Capacity-Maximization based Reversible Data Hiding for Depth Maps |
| 指導教授： |
鍾國亮
Kuo-Liang Chung |
| 口試委員: |
馮輝文
Huei-Wen Ferng 黃元欣 Yuan-Shin Hwang 花凱龍 Kai-Lung Hua 吳怡樂 Yi-Leh Wu |
| 學位類別： |
碩士 Master |
| 系所名稱： |
電資學院 - 資訊工程系 Department of Computer Science and Information Engineering |
| 論文出版年： | 2017 |
| 畢業學年度： | 106 |
| 語文別： | 英文 |
| 論文頁數： | 39 |
| 中文關鍵詞： | 彩圖加上深度圖編碼模型 、深度圖 、深度合成無誤差模型 、藏量的最大化 、無失真之彩現視角 |
| 外文關鍵詞： | Color plus depth coding model, Depth map, Depth no-synthesis error model, Maximization of embedding capacity, Lossless rendered view |
| 相關次數： | 點閱：178 下載：0 |
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在本論文中提出了一個新的可逆式資料隱藏法，此方法結合了深度合成無誤差模型及藏量最大化並應用於深度圖。本篇論文所提之可逆式資料隱藏方法的前半部分──深度合成無誤差模型──不會在彩現的虛擬視角中產生誤差，得到無失真之彩現視角。本篇論文所提之可逆式資料隱藏方法的後半部分，藉由完全地利用存在於深度圖中的大片平滑區域，我們提出一個新穎的遞迴方法使得藏量最大化。基於九個典型的3D測試深度圖，實驗結果證明，相較於五個優異的比較方法，所提之新穎且有效的可逆式資料隱藏法應用於深度圖時有顯著的藏量改善。
In this thesis, a new reversible data hiding (RDH) method, which joins the depth no-synthesis-error (DNOSE) model and the maximization of embedding capacity, for depth maps is proposed. The former part of the proposed RDH method, the DNOSE model, causes no distortion in the rendered virtual view, leading to the lossless rendered view. The latter part of the proposed RDH method, a novel recursive approach is proposed to maximize the embedding capacity by fully exploiting the large smooth areas existed in the depth map. Based on the nine typical 3D test depth maps, the experimental results demonstrated that the proposed novel and effective RDH method for depth maps has significant embedding capacity improvement when compared with the five state-of-the-art RDH methods.
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