Perceptual Quality Metrics (PVM)


Dr. Fan (Aaron) Zhang


Prof. David Bull, Dr. Dimitris Agrafiotis and Dr. Roland Baddeley






PVM Matlab code Download.


It is known that the human visual system (HVS) employs independent processes (distortion detection and artefact perception – also often referred to near-threshold supra-threshold distortion perception) to assess video quality for various distortion levels. Visual masking effects also play an important role in video distortion perception, especially within spatial and temporal textures.

Algorithmic diagram for PVM.
It is well known that small differences in textured content can be tolerated by the HVS. In this work, we employ the dual-tree complex wavelet transform (DT-CWT) in conjunction with motion analysis to characterise this tolerance within spatial and temporal textures. The DT-CWT has been found to be particularly powerful in this context due to its shift invariance and orientation selectivity properties. In highly distorted video content, for compressed material, blurring is one of the most commonly occuring artefacts. This is detected in our approach by comparing high frequency subband coefficients from the reference and distorted frames, also facilitated by the DT-CWT. This is motion-weighted in order to simulate the tolerance of the HVS to blurring in content with high temporal activity. Inspired by the previous work of Chandler and Hemamiand Larson and Chandler, thresholded differences (defined as noticeable distortion) and blurring artefacts are non-linearly combined using a modified geometric mean model, in which the proportion of each component is adaptively tuned. The performance of the proposed video metric is assessed and validated using the VQEG FRTV Phase I and the LIVE video databases, and shows clear improvements in correlation with subjective scores, over existing metrics such as PSNR, SSIM, VIF, VSNR, VQM and MOVIE, and in many cases over STMAD.


Figure: Scatter plots of subjective DMOS versus different video metrics on the VQEG database.
Figure: Scatter plots of subjective DMOS versus different video metrics on the LIVE video database.


  1. A Perception-based Hybrid Model for Video Quality Assessment F. Zhang and D. Bull, IEEE T-CSVT, June 2016.
  2. Quality Assessment Methods for Perceptual Video Compression F. Zhang and D. Bull, ICIP, Melbourne, Australia, September 2013.


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