Content-Based Video Quality Prediction for HEVC Encoded Videos Streamed Over Packet Networks
- Submitting institution
-
University of Plymouth
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 881
- Type
- D - Journal article
- DOI
-
10.1109/tmm.2015.2444098
- Title of journal
- IEEE Transactions on Multimedia
- Article number
- 8
- First page
- 1323
- Volume
- 17
- Issue
- 8
- ISSN
- 1520-9210
- Open access status
- Out of scope for open access requirements
- Month of publication
- -
- Year of publication
- 2015
- 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
-
4
- Research group(s)
-
-
- Citation count
- 38
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Video streaming services are dominating the Internet. Predicting video streaming quality is therefore key for service providers for quality monitoring and management. Video quality is affected by many factors including video content types, which is not well studied. In this paper, we propose a novel content type metric to quantify video sequences. We develop a video quality prediction model by bringing together video content types, codec compression, and network impairments. Tests show the model to be highly promising. The work contributed to secure an EU-grant (H2020 QoE-Net of a total of £3.1M with £547K to the University).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -