  Video quality assessment is a challenging problem having a critical
significance in the context of medical imaging. For instance, in laparoscopic
surgery, the acquired video data suffers from different kinds of distortion
that not only hinder surgery performance but also affect the execution of
subsequent tasks in surgical navigation and robotic surgeries. For this reason,
we propose in this paper neural network-based approaches for distortion
classification as well as quality prediction. More precisely, a Residual
Network (ResNet) based approach is firstly developed for simultaneous ranking
and classification task. Then, this architecture is extended to make it
appropriate for the quality prediction task by using an additional Fully
Connected Neural Network (FCNN). To train the overall architecture (ResNet and
FCNN models), transfer learning and end-to-end learning approaches are
investigated. Experimental results, carried out on a new laparoscopic video
quality database, have shown the efficiency of the proposed methods compared to
recent conventional and deep learning based approaches.
