3D visual discomfort prediction using low complexity disparity algorithms

Algorithms that predict the degree of visual discomfort experienced when viewing stereoscopic 3D (S3D) images usually first execute some form of disparity calculation. Following that, features are extracted on these disparity maps to build discomfort prediction models. These features may include, for example, the maximum disparity, disparity range, disparity energy, and other measures of the disparity distribution. Hence, the accuracy of prediction largely depends on the accuracy of disparity calculation. Unfortunately, computing disparity maps is expensive and difficult and most leading assessment models are based on features drawn from the outputs of high complexity disparity calculation algorithms that deliver high quality disparity maps. There is no consensus on the type of stereo matching algorithm that should be used for this type of model. Towards filling this gap, we study the relative performances of discomfort prediction models that use disparity algorithms having different levels of complexity. We also propose a set of new discomfort predictive features with good performance even when using low complexity disparity algorithms.