## 4 Experimental Results  
In order to evaluate the performance of the proposed method, the multi-view video plus depth sequences \"Ballet\" and \"Breakdancers\" provided by Microsoft Research are used in our experiment [30]. The sequence consists of eight viewpoints and each view contains 100 color images and corresponding depth images. The size of each frame is \(1024\times 768\). Camera parameters are known, including internal and external parameters. The synthesized virtual image is named after the tested sequence and projection information. For example, the sequence warped from the view 5 to view 4 of \"Ballet\" is named as BA54. The parameters of the proposed method are set as follows. Considering the size of the extended region, for BA54, BA56, BR43, and BR45, \(N\) is set to 97, and for BA52 and BR41, which have a large baseline, \(N\) is set to 217. In the \"Ballet\" sequence, a foreground object is far from another one, and there is a significant depth difference between the foreground and the adjacent background. But the in \"Breakdancers\" sequence, there are a series of objects with similar depth values. In this case, the preprocessing threshold \(T\) is set to 25 and 15 for \"Ballet\" and \"Breakdancers\", respectively. In this section, subjective evaluation and objective evaluation are compared among the proposed and previous methods, including [PERSON]'s exemplar-based image inpainting method [14], [PERSON]'s depth-aided inpainting method [15], divide-and-conquer hole-filling method [12], [PERSON]'s inpainting-based method [16], and [PERSON]'s disocclusions filling method [17]. In References [12; 14; 16; 17], and the proposed method, the virtual view synthesis needs only a single reference image and its depth image, while the method in Reference [15] is under the assumption that the depth image of the virtual view is given. The results of subjective comparisons are shown in the following figures. Figure 10a,b show the synthesized virtual images with the original DIBR algorithm for BA54 and BA52. It can be seen that cracks and pixel overlapping reduce the image quality. The rendering results of the modified 3D warping for BA54 and BA52 are shown in Figure 10c,d. Unwanted cracks and overlapping pixels are removed, then the remaining artifacts in the virtual image are the OFRs and disocclusions. The subjective quality comparison of the disocclusion filling results in the case of small baseline is shown in Figure 11. In [PERSON]'s method [14], foreground objects are sampled to fill the disocclusion, resulting a decline in the quality of the virtual view, as shown in Figure 11b. In [PERSON]'s method [15], although the calculation of priority is improved by introducing the depth variance of the patch, the existence of ghosts causes come defects in the disoccluded region, as shown in Figure 11c. The result of [PERSON]'s method [12] is shown in Figure 11d, which has a good effect on the simple background texture, but some artifacts appear among the boundaries of foreground. For the complex background texture, the edge structure may be distorted which affects the visual quality. In [PERSON]'s method [16], the priority consists of the confidence term and the depth term. While the hole filling starts from the background side, the lack of the data term makes the structure and texture difficult to maintain effectively, as shown in Figure 11e. In [PERSON]'s method [17], the calculation among the three items of priority is changed from multiplication to addition based on [15], and a depth image preprocessing method is introduced. The filling result of disocclusion is shown in Figure 11f. For depth image with multiple depth layers, the foreground and background objects cannot be accurately identified based on the inverse of the depth variance. Compared with the previous methods, the proposed inpainting method combined with the adaptive patch size can successfully protect the foreground boundary and show a better result in Figure 11g.