Objective video quality metrics application to video codecs comparisons: choosing the best for subjective quality estimation
This work addresses the problem of selecting optimal metrics for video codec comparisons, which is incremental as it refines existing methods rather than introducing new ones.
The paper compared different versions of objective video quality metrics (e.g., PSNR, SSIM, VMAF) to identify the most relevant ones for estimating subjective quality in video codec comparisons, using a dataset of encoded videos and visual scores from 2018 to 2021.
Quality assessment plays a key role in creating and comparing video compression algorithms. Despite the development of a large number of new methods for assessing quality, generally accepted and well-known codecs comparisons mainly use the classical methods like PSNR, SSIM and new method VMAF. These methods can be calculated following different rules: they can use different frame-by-frame averaging techniques or different summation of color components. In this paper, a fundamental comparison of various versions of generally accepted metrics is carried out to find the most relevant and recommended versions of video quality metrics to be used in codecs comparisons. For comparison, we used a set of videos encoded with video codecs of different standards, and visual quality scores collected for the resulting set of streams since 2018 until 2021