Visual discomfort assessment (VDA) on stereoscopic images is of fundamental importance for making decisions regarding visual fatigue caused by unnatural binocular alignment. Nevertheless, no solid framework exists to quantify this discomfort using models of the responses of visual neurons. Binocular vision is realized by means of neural mechanisms that subserve the sensorimotor control of eye movements. We propose a neuronal model-based framework called Neural 3D Visual Discomfort Predictor (N3D-VDP) that automatically predicts the level of visual discomfort experienced when viewing stereoscopic 3D (S3D) images. The N3D-VDP model extracts features derived by estimating the neural activity associated with the processing of binocular disparities. In this regard we deploy a model of disparity processing in the extra-striate middle temporal (MT) region of occipital lobe. We compare the performance of N3D-VDP with other recent VDA algorithms using correlations against reported subjective visual discomfort, and show that N3D-VDP is statistically superior to the other methods.
|Title of host publication
|2015 IEEE International Conference on Image Processing, ICIP 2015 - Proceedings
|IEEE Computer Society
|Number of pages
|Published - 2015 Dec 9
|IEEE International Conference on Image Processing, ICIP 2015 - Quebec City, Canada
Duration: 2015 Sept 27 → 2015 Sept 30
|Proceedings - International Conference on Image Processing, ICIP
|IEEE International Conference on Image Processing, ICIP 2015
|15/9/27 → 15/9/30
Bibliographical notePublisher Copyright:
© 2015 IEEE.
All Science Journal Classification (ASJC) codes
- Computer Vision and Pattern Recognition
- Signal Processing