Deep Image Quality Metric
- Day - Time: 10 October 2018, h.11:15
- Place: Area della Ricerca CNR di Pisa - Room: C-29
Image metrics based on human visual system (HVS) play a remarkable role in the evaluation of complex image processing algorithms. However, mimicking the HVS is typically complex, and it demands high computational resources (both in terms of time and memory) that limit their usage to a few applications or limit the size of the input data, making such metrics not fully attractive in real-world scenarios. To address these issues, we propose Deep Image Quality Metric (DIQM), a deep-learning approach to learn visual metric features such as predicting the probability of visibility changes and global image quality (mean-opinion-score). DIQM can reduce the computational costs of existing visual metrics by more than an order of magnitude.