  We propose a new prototype model for no-reference video quality assessment
(VQA) based on the natural statistics of space-time chips of videos. Space-time
chips (ST-chips) are a new, quality-aware feature space which we define as
space-time localized cuts of video data in directions that are determined by
the local motion flow. We use parametrized distribution fits to the bandpass
histograms of space-time chips to characterize quality, and show that the
parameters from these models are affected by distortion and can hence be used
to objectively predict the quality of videos. Our prototype method, which we
call ChipQA-0, is agnostic to the types of distortion affecting the video, and
is based on identifying and quantifying deviations from the expected statistics
of natural, undistorted ST-chips in order to predict video quality. We train
and test our resulting model on several large VQA databases and show that our
model achieves high correlation against human judgments of video quality and is
competitive with state-of-the-art models.
