No-reference video quality estimation based on machine learning for passive gaming video streaming applications
- Submitting institution
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Kingston University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 11-12-1346
- Type
- D - Journal article
- DOI
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10.1109/ACCESS.2019.2920477
- Title of journal
- IEEE Access
- Article number
- -
- First page
- 74511
- Volume
- 7
- Issue
- -
- ISSN
- 2169-3536
- Open access status
- Compliant
- Month of publication
- -
- Year of publication
- 2019
- URL
-
-
- Supplementary information
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- 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
-
-
- Research group(s)
-
-
- Citation count
- 7
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Since the proposed method delivers "no-reference" video quality estimation, it does not need the original content for the assessment. Moreover, as it is "lightweight" (limited complexity), it is a very suitable candidate to be used for gaming video streaming (e.g., Twitch, Facebook, and Youtube). Some of the research reported in this paper is now part of ITU standards contributions (Study Group 12 - Opinion model for gaming applications, G.OMG), involving the same authors.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -