On the Performance of DIBR Methods When Using Depth Maps from State-of-the-art Stereo Matching Algorithms

Adriano Q. de Oliveira; Thiago L. T. da Silveira; Marcelo Walter; Cláudio R. Jung

ICASSP 2019accepted0 citations

On the Performance of DIBR Methods When Using Depth Maps from State-of-the-art Stereo Matching Algorithms

Adriano Q. de Oliveira, Thiago L. T. da Silveira, Marcelo Walter, Cláudio R. Jung

Abstract

In this paper we compare the quality of synthesized views produced by four DIBR methods when fed by depth maps estimated by five state-of-the-art stereo matching algorithms. Also, we compute the correlation between four popular metrics for ranking stereo matching algorithms and two metrics commonly used to evaluate synthesized views (PSNR and SSIM) plus one specific for DIBR. Among our findings, we highlight that (i) PSNR and SSIM have a weak correlation with common stereo matching metrics, (ii) using ground-truth depth does not lead necessarily to the best DIBR result; and (iii) estimated depth maps present artifacts that affect differently DIBR methods.

BibTeX

@inproceedings{icassp2019_ontheperformance,
  title = {On the Performance of DIBR Methods When Using Depth Maps from State-of-the-art Stereo Matching Algorithms},
  author = {Adriano Q. de Oliveira and Thiago L. T. da Silveira and Marcelo Walter and Cláudio R. Jung},
  booktitle = {ICASSP 2019},
  year = {2019}
}