近來隨著視訊編碼與通信技術的進步，多媒體(multimedia) 通信應用視訊不僅只在被動地接收視訊資訊，而是強調能夠呈現在自然場景中的深度感和立體感，能夠提供人眼所見到真實場景的效果，因此三維視訊(3D video)處理技術已成為下世代多媒體的發展主流之一。然而，三維視訊編碼技術如多視角視訊編碼(Multi-view Video Coding, MVC)相較於傳統單一視角視訊畫面影像編碼，資料量更為龐大，運算複雜度極高，實際應用需升級軟硬體。因此使用分散式視訊編碼技術(Distributed Video Coding, DVC)將複雜計算移至解碼端，並結合了多視角視訊結合深度影像(Multi-view Video plus Depth, MVD )系統碼流，能更多利用深度影像關聯性並提供解碼端三維影像重建空間，因此有研究提出MDVDC系統架構(Multi-view Distributed Video plus Depth Coding, MDVDC)。本論文我們根據這個架構，提出改善視訊編碼品質的方法：(1)-提出深度對應亮度圖透視轉換演算法於多視角分散式視訊聯合解碼，利用時間域、視角間和影像深度多維度關聯性達到最好的輔資資訊品質，提升編碼效能；(2)-以不同畫面群結構，驗證深度對應亮度圖透視轉換演算應用於不同輔助資訊產生皆能有效提升影像品質，進而加速渦輪解碼時間。於畫面群為一(GOP=1)，Depth Homo-Fusion比其他方法好至0.3~3dB。實驗過程中於畫面群為二(GOP=2)，Depth Mapping SIFT-BMP提高較MCTI至少2.3~8.35dB、於ballet影片較SIFT-BMP提升0.1~0.2dB且於渦輪解碼時間最多較MCTI減少約20.05%，維持低於MVME運算複雜度。實驗結果顯示本研究所提方法能有效整合影像灰階與深度資訊之關連性，進一步改進編碼效能。

With the advance of video communication technology, the multimedia platform can not only receives and plays video streaming but also provides depth and stereo information of the natural scene for better visual perception. The 3D technologies play an important role of video innovation. The multi-view video coding (MVC) is an application of 3D video coder. However, the information amount of video data and the required computations for a multi-view system will be very large, as compared to single view videos. The distributed video coding (DVC) efficiently shifts computations to the decoder. The DVC decoder not only exploits inter- and intra view correlations but also utilizes depth information to enhance the quality of reconstructed images. We propose multi-view distributed video plus depth coding (MDVDC) to improve MDVC codec performance: (1) It applies depth and color perspective transform mapping algorithm to joint decoder and well utilizes temporal, interview and depth correlations to yield better side information (SI) images. (2) The proposed MDVDC algorithm can be applied to different GOPs and improve codec performance. It saves turbo decoding time. In comparisons, the Depth Homo-fusion is 0.3~3dB improved as compare to other methods when GOP=1. When GOP=2, the proposed Depth Mapping SIFT-BMP improved 2.3~8.35dB and 0.1~0.2dB as compare to MCTI and SIFT-BMP, respectively. It saves 20.05% decoding time as compares to MCTI.

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