A Perception-based Hybrid Model for Video Quality Assessment
- Submitting institution
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University of Bristol
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 94283460
- Type
- D - Journal article
- DOI
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10.1109/TCSVT.2015.2428551
- Title of journal
- IEEE Transactions on Circuits and Systems for Video Technology
- Article number
- -
- First page
- 1017
- Volume
- 26
- Issue
- 6
- ISSN
- 1051-8215
- Open access status
- Out of scope for open access requirements
- Month of publication
- May
- Year of publication
- 2016
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
1
- Research group(s)
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C - Visual Information Lab
- Citation count
- 21
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Presented the state-of-the-art in perceptual metrics, offering greater alignment with subjective assessments across a wider range of content than its competitors. Emerging from Bristol Vision Institute’s EPSRC Platform Grant (PI:Bull/EP/M000885/1) and EPSRC COMPPACT (PI:Bull/EP/J019291/1), PVM inspired perceptual decision making in ViSTRA (1st, 2017 IEEE International Grand Challenge on Video Compression). It's a key focus of our strategic relationship with Netflix (only UK university partner, J Sole, Netflix) underpinning enhancements to their Dynamic Optimization process via integration into their VMAF metric. It contributed to attracting Netflix, YouTube and BBC to partner in the £46m UKRI MyWorld Strength in Places programme (PI:Bull/2020).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -