三維（3D）視頻被廣泛認為是視覺媒體技術，使觀眾能夠感知場景的深度，最新三維視頻標準3D-HEVC需要精確的景深資訊。因此，對於未來3D廣播，如何在不同的場景條件下準確地估計和修補景深圖是一門值得研究的議題。本文提出了幾種方法來處理低分辨率情況下的景深估計和強化。此外，我們還討論了低分辨率情況下，紋理較弱、匹配精度較低、不連續性、光照差異和遮擋等情況下的不同變化。換句話說，這些問題可以在不使用視頻信號的超分辨率技術和多幀的情況下進行研究。
為了整理低分辨率的景深估計和增強，我們提出了一種立體匹配演算法和三種景深強化系統，包括與三基色（TCC）普查和基於三重圖像精鍊的立體匹配演算法、先進的多邊濾波器（AMF）、一致性引導濾波器（CF）、有效引導上採樣（PGU）濾波器耦合自適應梯度融合（AGF）濾波器。
實驗結果表明，本研究所提出之景深圖預測及強化系統與現有方法相比，我們所提出的方法在視覺和主觀度量上都表現出更好的質量，並且實現了視覺上可比較的結果。

The three-dimensional (3D) video is widely recognized as a visual media technique which enables viewers to perceive the depth in a scene. Most 3D video standards, such as 3D-HEVC, all need depth information. For 3D broadcasting, how to accurately estimate the depth map and improve its performance the under different scene conditions becomes important. In this dissertation, several effective methods have been proposed to deal with the low resolution situation for depth estimation and enhancement. Moreover, we also discuss the different variations under the low resolution challenges such as weak texture, less matching accuracy, discontinuities, illumination difference and occlusions and so on. In other words, the problems can be successfully solved without using supper-resolution techniques and multiple frames from video signals.
In order to realize low-resolution depth estimation and its enhancements, we proposed one stereo matching algorithm and three novel depth enhancement methods to become a complete depth estimation system. The complete system includes stereo matching with trinary cross color (TCC) census and triple image-based refinements, advanced multilateral filters (AMF), consistency-guided filter (CF), potency guided upsampling (PGU) filter coupled with adaptive gradient fusion (AGF) filter.
Experimental results show that the proposed methods perform better qualities in both visual and subjective metrics than the classic methods and achieve visually comparable results to some time-consuming methods.

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