  Predicting human scanpaths when exploring panoramic videos is a challenging
task due to the spherical geometry and the multimodality of the input, and the
inherent uncertainty and diversity of the output. Most previous methods fail to
give a complete treatment of these characteristics, and thus are prone to
errors. In this paper, we present a simple new criterion for scanpath
prediction based on principles from lossy data compression. This criterion
suggests minimizing the expected code length of quantized scanpaths in a
training set, which corresponds to fitting a discrete conditional probability
model via maximum likelihood. Specifically, the probability model is
conditioned on two modalities: a viewport sequence as the deformation-reduced
visual input and a set of relative historical scanpaths projected onto
respective viewports as the aligned path input. The probability model is
parameterized by a product of discretized Gaussian mixture models to capture
the uncertainty and the diversity of scanpaths from different users. Most
importantly, the training of the probability model does not rely on the
specification of "ground-truth" scanpaths for imitation learning. We also
introduce a proportional-integral-derivative (PID) controller-based sampler to
generate realistic human-like scanpaths from the learned probability model.
Experimental results demonstrate that our method consistently produces better
quantitative scanpath results in terms of prediction accuracy (by comparing to
the assumed "ground-truths") and perceptual realism (through machine
discrimination) over a wide range of prediction horizons. We additionally
verify the perceptual realism improvement via a formal psychophysical
experiment and the generalization improvement on several unseen panoramic video
datasets.
