  This paper describes a quality assessment model for perceptual video
compression applications (PVM), which stimulates visual masking and
distortion-artefact perception using an adaptive combination of noticeable
distortions and blurring artefacts. The method shows significant improvement
over existing quality metrics based on the VQEG database, and provides
compatibility with in-loop rate-quality optimisation for next generation video
codecs due to its latency and complexity attributes. Performance comparison are
validated against a range of different distortion types.
