Are you an EPFL student looking for a semester project?
Work with us on data science and visualisation projects, and deploy your project as an app on top of Graph Search.
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.
Arash Amini, Hatef Otroshi Shahreza
Touradj Ebrahimi, Rayan Daod Nathoo, Laurent Deillon, Henrique Piñeiro Monteagudo