  We propose a new model for no-reference video quality assessment (VQA). Our
approach uses a new idea of highly-localized space-time (ST) slices called
Space-Time Chips (ST Chips). ST Chips are localized cuts of video data along
directions that \textit{implicitly} capture motion. We use
perceptually-motivated bandpass and normalization models to first process the
video data, and then select oriented ST Chips based on how closely they fit
parametric models of natural video statistics. We show that the parameters that
describe these statistics can be used to reliably predict the quality of
videos, without the need for a reference video. The proposed method implicitly
models ST video naturalness, and deviations from naturalness. We train and test
our model on several large VQA databases, and show that our model achieves
state-of-the-art performance at reduced cost, without requiring motion
computation.
