Repository logo

Infoscience

  • English
  • French
Log In
Logo EPFL, École polytechnique fédérale de Lausanne

Infoscience

  • English
  • French
Log In
  1. Home
  2. Academic and Research Output
  3. Conferences, Workshops, Symposiums, and Seminars
  4. Benchmarking of quality metrics on ultra-high definition video sequences
 
Loading...
Thumbnail Image
conference paper not in proceedings

Benchmarking of quality metrics on ultra-high definition video sequences

Hanhart, Philippe  
•
Korshunov, Pavel
•
Ebrahimi, Touradj  
2013
18th International Conference on Digital Signal Processing

The performance of objective quality metrics for high-definition (HD) video sequences is well studied, but little is known about their performance for ultra-high definition (UHD) video sequences. This paper analyzes the performance of several common objective quality metrics (PSNR, VSNR, SSIM, MS-SSIM, VIF, and VQM) on three different 4K UHD video sequences using subjective scores as ground truth. The findings confirm the content-dependent nature of most metrics (with VIF being the only exception), which has been reported previously for standard and high resolution video sequences. PSNR showed the lowest correlation with ground truth quality scores when the analysis was performed for all contents at once and thus is not recommended as a general metric for video quality, while VIF showed the highest Pearson (0.83) and Spearman (0.87) correlation coefficients and may be used as a general purpose metric. On the other hand, all studied metrics were accurate in distinguishing different quality levels for the same content. The results of several fittings between metric values and subjective ground truth scores demonstrated that logistic fitting provides the highest correlation. The results also indicated a shift in metrics values between synthetic and natural contents.

  • Files
  • Details
  • Metrics
Loading...
Thumbnail Image
Name

DSP2013.pdf

Type

Preprint

Access type

openaccess

Size

2.11 MB

Format

Adobe PDF

Checksum (MD5)

bff5eb138d5346b0cb4b0e7e047cdf26

Logo EPFL, École polytechnique fédérale de Lausanne
  • Contact
  • infoscience@epfl.ch

  • Follow us on Facebook
  • Follow us on Instagram
  • Follow us on LinkedIn
  • Follow us on X
  • Follow us on Youtube
AccessibilityLegal noticePrivacy policyCookie settingsEnd User AgreementGet helpFeedback

Infoscience is a service managed and provided by the Library and IT Services of EPFL. © EPFL, tous droits réservés