Classification of GPU rendering errors with Artificial Neural Networks
(2019) In Master's Theses in Mathematical Sciences FMAM05 20191Mathematics (Faculty of Engineering)
- Abstract (Swedish)
- Image quality metrics are used to evaluate the percieved quality of processed images. Differences in hardware between graphics processors contribute to noise during quality evaluation. In this masters thesis paper we train and evaluate neural networks as metrics to evaluate GPU rendering quality. The neural networks can successfully ignore the rendering noise that occurs when the test and reference frames are rendered by different GPUs. This reduces tedious human interaction which requires manual updates of reference-frames during quality testing.
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/8981960
- author
- Hansson, Alexander LU and Moodie, Peter
- supervisor
- organization
- course
- FMAM05 20191
- year
- 2019
- type
- H2 - Master's Degree (Two Years)
- subject
- keywords
- Machine learning, Artificial Neural Networks, Image Analysis, Image quality metrics, Graphics processors, Quality testing
- publication/series
- Master's Theses in Mathematical Sciences
- report number
- LUTFMA-3386-2019
- ISSN
- 1404-6342
- other publication id
- 2019:E30
- language
- English
- id
- 8981960
- date added to LUP
- 2019-07-16 13:35:54
- date last changed
- 2019-07-16 13:35:54
@misc{8981960, abstract = {{Image quality metrics are used to evaluate the percieved quality of processed images. Differences in hardware between graphics processors contribute to noise during quality evaluation. In this masters thesis paper we train and evaluate neural networks as metrics to evaluate GPU rendering quality. The neural networks can successfully ignore the rendering noise that occurs when the test and reference frames are rendered by different GPUs. This reduces tedious human interaction which requires manual updates of reference-frames during quality testing.}}, author = {{Hansson, Alexander and Moodie, Peter}}, issn = {{1404-6342}}, language = {{eng}}, note = {{Student Paper}}, series = {{Master's Theses in Mathematical Sciences}}, title = {{Classification of GPU rendering errors with Artificial Neural Networks}}, year = {{2019}}, }