Projects per year
Abstract
Objective quality measures based on machine
learning (ML) require fewer computations and
are less affected by inaccuracies in the HVS
models. But they may also yield less transparent
quality predictions when the ML responses are
difficult to interpret. The absence of
interpretability may disguise serious
vulnerabilities in the design of the objective
quality measure.
learning (ML) require fewer computations and
are less affected by inaccuracies in the HVS
models. But they may also yield less transparent
quality predictions when the ML responses are
difficult to interpret. The absence of
interpretability may disguise serious
vulnerabilities in the design of the objective
quality measure.
| Original language | English |
|---|---|
| Place of Publication | VQEG eLetter |
| Publisher | Video Quality Experts Group |
| Number of pages | 5 |
| Volume | 1 |
| Edition | 2 |
| Publication status | Published - Dec 2014 |
Fingerprint
Dive into the research topics of 'Reliably combining quality indicators'. Together they form a unique fingerprint.Projects
- 1 Finished
-
SRP11: Strategic Research Programme: Processing of large scale multi-dimensional, multi-spectral, multi-sensorial and distributed data (M³D²)
Schelkens, P. (Administrative Promotor), Deligiannis, N. (Co-Promotor), Jansen, B. (Co-Promotor), Kuijk, M. (Co-Promotor), Munteanu, A. (Co-Promotor), Sahli, H. (Co-Promotor), Steenhaut, K. (Co-Promotor), Stiens, J. (Co-Promotor), Schelkens, P. (Administrative Promotor), Cornelis, J. P. (Co-Promotor), Kuijk, M. (Co-Promotor), Munteanu, A. (Co-Promotor), Sahli, H. (Co-Promotor), Stiens, J. (Co-Promotor) & Vounckx, R. (Co-Promotor)
1/11/12 → 31/12/23
Project: Fundamental
Cite this
- APA
- Author
- BIBTEX
- Harvard
- Standard
- RIS
- Vancouver