A Full Reference Quality Metric for Compressed Video Based on Mean Squared Error and Video Content

摘要

Visual quality of compressed video sequences depends on factors including spatial texture content and cognition-based factors such as prior knowledge and task in hand. The MOSp metric is a full reference objective quality metric which predicts perceived quality of sequences with video compression-induced impairments based on the spatial texture content and the mean squared error between original and compressed video sequences. In this paper, we extend the MOSp metric to incorporate cognition-based factors to identify regions in a video scene that attract human attention. The proposed metric has been tested on a variety of multimedia sequences of common intermediate format resolution compressed at a wide range of bitrates using the H.264/AVC coding standard. This metric shows a higher correlation with mean opinion score (MOS) than popular metrics, such as peak signal-to noise ratio, National Telecommunications and Information Administration/Institute for Telecommunication Sciences video quality metric, PSNRplus, and the Yonsei University metric. Results also show that by extending the MOSp metric to incorporate cognition-based factors such as skin information, its correlation with subjective scores (MOS) can be significantly improved in video content where humans are present. This algorithm is particularly useful for real-time quality estimation of multimedia sequences with block-based video compression-induced impairments because all the parameters of the metric can be calculated automatically with a modest amount of processing overhead.