Objective Video Quality Assessment Based on Perceptually Weighted Mean Squared Error

Abstract

Object quality assessment for compressed video is critical to various video compression systems that are essential in the video delivery and storage. Although mean squared error (MSE) is computationally simple, it may not be accurate to reflect the perceptual quality of compressed videos, which are also affected dramatically by the characteristics of the human visual system (HVS), such as contrast sensitivity, visual attention, and masking effect. In this paper, a video quality metric is proposed based on perceptually weighted MSE. A low-pass filter is designed to model the contrast sensitivity of the HVS with the consideration of visual attention. The imperceptible distortion is adaptively removed in the salient and nonsalient regions. To quantitatively measure the masking effect, the randomness of video content is proposed in both the spatial and temporal domains. Since the masking effect highly depends on the regularity of structure and motion in the spatial and temporal directions, the video signal is modeled as a linear dynamic system, and the prediction error of future frames from previous frames is used as randomness to measure the significance of masking. The relation is investigated between MSE and perceptual quality scores across various contents, and a masking modulation model is proposed to compensate the impact of the masking effect on the MSE. The performance of the proposed quality metric is validated on three video databases with various compression distortions. The experimental results demonstrate that the proposed algorithm outperforms other benchmark quality metrics.