A Perception-based Hybrid Model for Video Quality Assessment

Research output: Contribution to journalArticle (Academic Journal)peer-review

40 Citations (Scopus)


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. In this paper, a novel perception-based hybrid model for video quality assessment is presented. This simulates the HVS perception process by adaptively combining
noticeable distortion and blurring artefacts using an enhanced non-linear model. Noticeable distortion is defined by thresholding absolute differences using spatial and temporal tolerance maps which characterise texture masking effects, and this makes a significant contribution to quality assessment when the quality of the distorted video is similar to that of the original video. Characterisation of blurring artefacts, estimated by computing high frequency energy variations and weighted with motion speed, is found to further improve metric performance. This is especially true for low quality cases. All stages of our model exploit the orientation selectivity and shift invariance properties of the Dual Tree ComplexWavelet Transform. This not only helps to improve performance but also offers the potential for new low complexity in-loop application. Our approach is evaluated on both the VQEG FRTV Phase I and the LIVE video databases. The resulting overall performance is superior to existing metrics, exhibiting statistically better or equivalent performance with significantly lower complexity.
Original languageEnglish
Pages (from-to)1017-1028
JournalIEEE Transactions on Circuits and Systems for Video Technology
Issue number6
Early online date1 May 2016
Publication statusPublished - 2 Jun 2016


  • Video metrics
  • quality assessment
  • visual masking
  • blurring detection


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  • Vision for the Future-Full

    Bull, D. R.


    Project: Research


    Bull, D. R.


    Project: Research

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