A virtual view PSNR estimation method for 3-D Videos

Hui YUAN, Sam KWONG, Xu WANG, Yun ZHANG, Fengrong LI

Research output: Journal PublicationsJournal Article (refereed)peer-review

32 Citations (Scopus)

Abstract

In three-dimensional videos (3-DVs) with n-view texture videos plus n-view depth maps, virtual views can be synthesized from neighboring texture videos and the associated depth maps. To evaluate the system performance or guide the rate-distortion-optimization process of 3-DV coding, the distortion/PSNR of the virtual view should be calculated by measuring the quality difference between the virtual view synthesized by compressed 3-DVs with one synthesized by uncompressed 3-DVs, which increases the complexity of a 3-DV system. In order to reduce the complexity of 3-DV system, it is better to estimate virtual view distortions/PSNR directly without rendering virtual views. In this paper, the virtual view synthesis procedure and the distortion propagation from existing views to virtual views are analyzed in detail, and then a virtual view distortion/PSNR estimation method is derived. Experimental results demonstrate that the proposed method could estimate PSNRs of virtual views accurately. The squared correlation coefficient and root of mean squared error between the estimated PSNRs by the proposed method and the actual PSNRs are 0.998 and 2.012 on average for all the tested sequences. Since the proposed method is implemented row-by-row independently, it is also friendly for parallel design. The execute time for each row of pictures with 1024×768 resolution is only 0.079 s, while for pictures with 1920×1088 resolution it is only 0.155 s.
Original languageEnglish
Pages (from-to)134-140
JournalIEEE Transactions on Broadcasting
Volume62
Issue number1
Early online date6 Nov 2015
DOIs
Publication statusPublished - Mar 2016
Externally publishedYes

Keywords

  • 3DV
  • Distortion estimation
  • video coding

Fingerprint

Dive into the research topics of 'A virtual view PSNR estimation method for 3-D Videos'. Together they form a unique fingerprint.

Cite this