| Abstract
Video quality metrics are intended to replace
human evaluation with evaluation by machine. To accurately simulate human
judgement, they must include some aspects of the human visual system.
In this paper we present a class of low-complexity
video quality metrics based on the Standard Spatial Observer (SSO).
In these metrics, the basic SSO model is improved with several additional
features from the current human vision models.
To evaluate the metrics, we make use of
the data set recently produced by the Video Quality Experts Group (VQEG),
which consists of subjective ratings of 160 samples of digital video covering
a wide range of quality. For each metric we examine the correlation between
its predictions and the subjective ratings.
The results show that SSO-based models
with local masking obtain the same degree of accuracy as the best metric
considered by VQEG (P5), and
significantly better correlations than
the other VQEG models. The results suggest that local masking is a key
feature to improve the correlation of the basic SSO model. |
