In this paper, we propose a new depth image-based rending scheme for 3DTV applications, where a background sprite model is utilized for dis-occlusion/hole filling purpose. Dissimilar to traditional spatial (e.g., interpolation or inpainting) and temporal methods, our algorithm is capable of recovering the holes with true background information by incrementally integrating the spatial and temporal information of the video in a unified background sprite model. The technique of background sprite model construction in this paper is featured of resolving camera motions existing in most of the consumer videos, accurate registration of multiframe information, and efficient memory use and computation in realistic 3DTV application. To register/stitch each input frame to the background sprite model accurately, foreground removal considering color and depth information, and an adaptive key-frame-based scheme are developed for transform computation. Experimental results show that our proposed scheme has a large temporal reference distance and can retrieve true background information accurately, thus leading to better quality after novel view synthesis compared to existing spatial or spatio-temporal algorithms, especially for videos with significant camera motions or complex backgrounds.

In this paper, we propose a new depth image-based rending (DIBR) scheme for 3DTV applications, which is based on temporal hole filling with sprite generation. Dissimilar to traditional methods (e.g., spatial interpolation and inpainting), temporal information is utilized to recover the holes with true background information. The proposed scheme is composed of two parts: sprite generation and virtual view synthesis. To collect sufficiently temporal information, a key-frame-based sprite generation algorithm was developed to incrementally and accurately fuse the background color and depth information from successive frames. Then the holes in the synthesized virtual view can be recovered by referring to the constructed sprite models. Experiments demonstrate that our proposed scheme is capable of achieving good results and outperforms some existing methods subjectively and objectively.