## 4 Experimental Results
Figure 11: Disocclusion filling results in the case of small baseline: (**a**) Disocclusions after DIBR; (**b**) [PERSON]’s method; (**c**) [PERSON]’s method; (**d**) [PERSON]’s method; (**e**) [PERSON]’s method; (**f**) [PERSON]’s method; (**g**) the proposed method; and (**h**) ground truth. Figure 10: Visual quality comparison of 3D warping: (**a**) Synthesized result of the original 3D warping for BA54; (**b**) synthesized result of the original 3D warping for BA52; (**c**) synthesized result of the modified 3D warping for BA54; and (**d**) synthesized result of the modified 3D warping for BA52. The results of OFRs filling are shown in Figure 12. It can be seen the proposed method outperforms the previous method. In the proposed method, OFRs are filled by the neighbor regions in the reference image, so that the penetration problem can be avoided, while the results of other methods include some artifacts, for example, the foreground object in the other region is sampled to fill the OFRs and the error texture is spread. For BA52 and BR41, due to the increase of baseline distance, disocclusions filling become more challenging. The quality comparison is shown in Figure 13. Although the area of the disocclusion becomes larger and part of the hole is no longer adjacent to the foreground, the proposed method still successfully maintains the boundary between the foreground and the background, and the filling result is most similar to the real texture. In order to evaluate the objective performance of the proposed method, in our experiment, peak signal-noise ratio (PSNR) and structural similarity (SSIM) [31] are used to evaluate the degree of similarity between the synthesized image and the ground truth at the target view. PSNR is used to  
Figure 12: OFRs filling results: (**a**) OFRs after DIBR; (**b**) [PERSON]’s method; (**c**) [PERSON]’s method; (**d**) [PERSON]’s method; (**e**) [PERSON]’s method; (**f**) [PERSON]’s method; (**g**) the proposed method; and (**h**) ground truth. Figure 13: Disocclusion filling results in the case of large baseline: (**a**) Disocclusions after DIBR; (**b**) [PERSON]’s method; (**c**) [PERSON]’s method; (**d**) [PERSON]’s method; (**e**) [PERSON]’s method; (**f**) [PERSON]’s method; (**g**) the proposed method; and (**h**) ground truth. measure the squared intensity differences of pixels in the two images, and SSIM is used to measure the structural similarity between the synthesized image and the ground truth. The higher PSNR and SSIM values mean the higher quality of the synthesized image. The average PSNR and SSIM for \"Ballet\" and \"Breakdancers\" are shown in Tables 1 and 2. The results show that the proposed method achieves the overall best results. In the comparison of PSNR, the proposed method obtains 0.30-4.09 dB and 0.28-1.83 dB better results than the previous methods on \"Ballet\" and \"Breakdancers\", respectively. The SSIM gains of the proposed method over the previous methods on \"Ballet\" and \"Breakdancers\" are 0.0011-0.0252 and 0.0025-0.0108, respectively. The results of the PSNR and SSIM comparison for each frame are shown in Figure 14. The proposed method always shows the best results for both large baseline distance and small baseline distance. The proposed method is running on a PC and implemented via MATLAB and Visual Studio. In addition to the limitations of hardware devices, the improvement of virtual image quality increases the computational complexity of the proposed method. In our method, the 3D warping process is performed four times and the image inpainting takes a lot of time, especially for priority calculation and best matching patch searching. Although we have reduced some computational amount by limiting the source region, our method still has more computational complexity than the original DIBR algorithm. On the one hand, the GPU- accelerated algorithms can be applied to each step of our method, and the hardware acceleration scheme provides the possibility for real-time application of the algorithm.