  Video quality assessment (VQA) is now a fast-growing subject, maturing in the
full reference (FR) case, yet challenging in the exploding no reference (NR)
case. We investigate variants of the popular VMAF video quality assessment
algorithm for the FR case, using both support vector regression and feedforward
neural networks. We extend it to the NR case, using some different features but
similar learning, to develop a partially unified framework for VQA. When fully
trained, FR algorithms such as VMAF perform very well on test datasets,
reaching 90%+ match in PCC and SRCC; but for predicting performance in the
wild, we train/test from scratch for each database. With an 80/20 train/test
split, we still achieve about 90% performance on average in both PCC and SRCC,
with up to 7-9% gains over VMAF, using an improved motion feature and better
regression. Moreover, we even get decent performance (about 75%) if we ignore
the reference, treating FR as NR, partly justifying our attempts at
unification. In the true NR case, we reduce complexity vs. leading recent
algorithms VIDEVAL, RAPIQUE, yet achieve performance within 3-5%. Moreover, we
develop a method to analyze the saliency of features, and conclude that for
both VIDEVAL and RAPIQUE, a small subset of their features are providing the
bulk of the performance. In short, we find encouraging improvements in
trainability in FR, while constraining training complexity against leading
methods in NR, elucidating the saliency of features for feature selection.
