  We propose a perceptual video quality assessment (PVQA) metric for distorted
videos by analyzing the power spectral density (PSD) of a group of pictures.
This is an estimation approach that relies on the changes in video dynamic
calculated in the frequency domain and are primarily caused by distortion. We
obtain a feature map by processing a 3D PSD tensor obtained from a set of
distorted frames. This is a full-reference tempospatial approach that considers
both temporal and spatial PSD characteristics. This makes it ubiquitously
suitable for videos with varying motion patterns and spatial contents. Our
technique does not make any assumptions on the coding conditions, streaming
conditions or distortion. This approach is also computationally inexpensive
which makes it feasible for real-time and practical implementations. We
validate our proposed metric by testing it on a variety of distorted sequences
from PVQA databases. The results show that our metric estimates the perceptual
quality at the sequence level accurately. We report the correlation
coefficients with the differential mean opinion scores (DMOS) reported in the
databases. The results show high and competitive correlations compared with the
state of the art techniques.
