Abstract
Improving the users' Quality of Experience (QoE) in modern 3D Multimedia Systems is a challenging proposition, mainly due to our limited knowledge of 3D image Quality Assessment algorithms. While subjective QoE methods would better reflect the nature of human perception, these are not suitable in real-time automation cases. In this paper we tackle this issue from a new angle, using deep learning to make predictions on the user's QoE rather than trying to measure it through deterministic algorithms. We benchmark our method, dubbed Quality of Experience for 3D images through Factored Third Order Restricted Boltzmann Machine (Q3D-RBM), with subjective QoE methods, to determine its accuracy for different types of 3D images. The outcome is a Reduced Reference QoE assessment process for automatic image assessment and has significant potential to be extended to work on 3D video assessment.
| Original language | English |
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| Title of host publication | Proceedings of the IEEE International Conference on Image Processing 2014 (ICIP 2014), 27-30 October 2014, Paris, France |
| Place of Publication | Piscataway |
| Publisher | Institute of Electrical and Electronics Engineers |
| Pages | 758-762 |
| DOIs | |
| Publication status | Published - 2014 |
| Event | 21st IEEE International Conference on Image Processing (ICIP 2014) - Paris, France Duration: 27 Oct 2014 → 30 Oct 2014 Conference number: 21 http://www.icip2014.org/ |
Conference
| Conference | 21st IEEE International Conference on Image Processing (ICIP 2014) |
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| Abbreviated title | ICIP 2014 |
| Country/Territory | France |
| City | Paris |
| Period | 27/10/14 → 30/10/14 |
| Internet address |