# Virtual View Synthesis Based on Asymmetric Bidirectional DIBR for 3D Video and Free Viewpoint Video
## 3 Experimental Results and Discussion
### Subjective and Objective Evaluation of the Synthesized View  
In order to evaluate the performance of the proposed method subjectively and objectively, in this section the results of the proposed method are compared with [PERSON]'s method [16], [PERSON]'s method [18], [PERSON]'s method [19], [PERSON]'s method [26] and [PERSON]'s method [27]. For video sequence rendering, the results of subjective comparison between the proposed and previous methods are shown in Figure 11. In terms of visual quality, the proposed method performs better than others. In [PERSON]'s method, as the depth information is not considered, the hole-filling process is performed on both the foreground and background sides, resulting in the propagation of the foreground texture, as shown in Figure 11a. In [PERSON]'s method, affected by the depth error, some defects appear around the foreground objects, as shown in Figure 11b. In [PERSON]'s method, depth preprocessing is introduced to reduce ghosts, but some unexpected foreground textures are sampled to fill the disocclusion due to the mismatch of depth value, as shown in Figure 11c. In [PERSON]'s method, horizontal background extrapolation is very sensitive to artifacts, especially when both sides of the disocclusion are foreground pixels, as shown in Figure 11d. Furthermore, pixel-based extrapolation looks unnatural and is not suitable for complex textures. In [PERSON]'s method, some unreasonable regions are produced, as shown in Figure 11e. This is because the level regularity term formed by the inverse variance cannot accurately distinguish the foreground and background, especially for the multiple depth layers. The proposed method successfully removes most of the artifacts and shows more plausible results in Figure 11f. The depth-guided postprocessing method can effectively maintain the edges of foreground objects without introducing blurring regions. Figure 11: Visual quality comparison results of synthesized view for MVD sequences: (**a**) [PERSON]’s method; (**b**) [PERSON]’s method; (**c**) [PERSON]’s method; (**d**) [PERSON]’s method; (**e**) [PERSON]’s method; (**f**) proposed method; and (**g**) ground truth. Figure 10: Rendering time comparisons on testing data sets: (**a**) multiview video-plus-depth (MVD) sequences; and (**b**) still-image data sets. In terms of objective evaluation, peak signal-to-noise ratio (PSNR), structural similarity (SSIM) [38], feature similarity index (color) (FSIMc) [39], and visual saliency-induced index (VSI) [40] are used in our experiments to evaluate the objective quality of the synthesized views. PSNR measures the difference in pixels between two images. SSIM compares the structural similarity of the two images. FSIMc and VSI focus on the gradient and color features. The objective comparison results of the seven synthesized views by different methods are shown in Tables 3 and 4. Higher PSNR value represents better image quality, while SSIM, FSIMc, and VSI are all normalized to 0-1. The results show that the proposed method achieves the best overall performance. taking the scenario BA3 for example, the proposed method surpasses the previous methods by 0.75-1.69 dB in average PSNR. Likewise, there is evident promotion in terms of SSIM, FSIMc, and VSI. For image sequence rendering, the subjective comparison results are shown in Figure 12. Based on the parallel configuration, most of the OFRs are filled in the merging process. In disocclusion filling, the results of the proposed method look the most likely the truth textures, while other methods contain some unrealistic contents and foreground penetration. In terms of objective evaluation, Tables 5 and 6 show the comparison results for the still-image sequences. The proposed algorithm obtains a better evaluation on the basis of the four indicators, proving that the proposed method is robust and can adapt to the scene changes to some extent.