The quality assessment of Depth-Image-Based-Rendering (DIBR) synthesized views is very challenging owing to the new types of distortions, thus the traditional 2D quality metrics may fail to evaluate the quality of the synthesized views. In this paper, we propose a full-reference metric to assess the quality of DIBR synthesized views. Firstly, we notice that the object shift in the synthesized view is approximately linear, an affine transformation is used to warp the pixel in the reference image to the corresponding position in the distorted image. Besides, since the synthesis distortions mainly happen in the dis-occluded areas, a dis-occlusion mask obtained from the depth map in the original viewpoint is used to weight the final distortions between the synthesized image and the reference image. The experimental results on IRCCyN/IVC DIBR image database show that the proposed weighted PSNR (PSNR') outperforms the state-of-the-art DIBR synthesized view dedicated metrics: 3DSwIM, VSQA, MP-PSNR, MW-PSNR and earns a gain of 36.85% (in terms of PLCC) over PSNR. The weighted SSIM (SSIM') earns a gain of 13.33% (in terms of PLCC) compared to SSIM.
Shishun Tian, Lu Zhang, Luce Morin, Olivier Deforges, "A full-reference Image Quality Assessment metric for 3D Synthesized Views" in Proc. IS&T Int’l. Symp. on Electronic Imaging: Image Quality and System Performance XV, 2018, pp 366-1 - 366-5, https://doi.org/10.2352/ISSN.2470-1173.2018.12.IQSP-366