Q-STAR:A Perceptual Video Quality Model Considering Impact of Spatial, Temporal, and Amplitude Resolutions

TL;DR

This paper presents a perceptual video quality model that accurately predicts the impact of spatial, temporal, and amplitude resolutions on compressed video quality, validated through extensive subjective testing on mobile devices.

Contribution

The paper introduces a new model capturing the joint effects of spatial, temporal, and amplitude resolutions on video quality with only three content-dependent parameters.

Findings

The impact of SR, TR, and QS can be modeled by simple functions with a single parameter each.

The decay rates of quality with respect to SR, TR, and QS are independent of each other.

The model achieves a high correlation (PCC 0.991) with subjective quality ratings.

Abstract

In this paper, we investigate the impact of spatial, temporal and amplitude resolution (STAR) on the perceptual quality of a compressed video. Subjective quality tests were carried out on a mobile device. Seven source sequences are included in the tests and for each source sequence we have 27 test configurations generated by JSVM encoder (3 QP levels, 3 spatial resolutions, and 3 temporal resolutions), resulting a total of 189 processed video sequences (PVSs). Videos coded at different spatial resolutions are displayed at the full screen size of the mobile platform. Subjective data reveal that the impact of spatial resolution (SR), temporal resolution (TR) and quantization stepsize (QS) can each be captured by a function with a single content-dependent parameter. The joint impact of SR, TR and QS can be accurately modeled by the product of these three functions with only three parameters. We further find that the quality decay rates with SR and QS, respectively are independent of TR, and likewise, the decay rate with TR is independent of SR and QS, respectively. However, there is a significant interaction between the effects of SR and QS. The overall quality model is further validated on five other datasets with very high accuracy. The complete model correlates well with the subjective ratings with a Pearson Correlation Coefficient (PCC) of 0.991.