Perceptual quality metric for compressed videos
The paper proposes an objective perceptual video quality metric to assess the perceived quality of digital videos automatically. Traditionally, peak signal-to-noise ratio (PSNR) has been used to represent the quality of a compressed video sequence. However, PSNR has been found to correlate poorly with subjective quality ratings, particularly at much lower bit rates and frame rates. Computational models are applied to emulate human visual perception based on block-fidelity, content richness fidelity, spatial-textural, colour, and temporal maskings. The proposed video quality metric has been tested on CIF and QCIF video sequences compressed at various bit rates and frame rates. It has been shown to give significantly better correlation to human perception than PSNR.