A novel perceptual quality metric for video compression

Modern video compression systems make optimum coding decisions based on rate-distortion performance. Typically the distortion is evaluated as a mathematical error measurement, such as mean squared error (MSE), between the original and compressed video sequences. However, simple automatic error measurements such as MSE do not correlate well with perceptual quality, leading to sub-optimal coding decisions. In this paper we present a new subjective video quality metric that predicts subjective quality of compressed video using temporal and spatial masking information and the MSE between the original and compressed video sequences. The results show that this metric can predict perceptual quality with significantly higher correlation compared to popular metrics such as PSNR, VSSIM and PSNRplus. This algorithm is particularly useful for real-time, accurate perceptual video quality estimation in video applications because all the parameters of the metric can be calculated with a minimal amount of processing overhead.