  Video quality assessment (VQA) methods focus on particular degradation types,
usually artificially induced on a small set of reference videos. Hence, most
traditional VQA methods under-perform in-the-wild. Deep learning approaches
have had limited success due to the small size and diversity of existing VQA
datasets, either artificial or authentically distorted. We introduce a new
in-the-wild VQA dataset that is substantially larger and diverse: KonVid-150k.
It consists of a coarsely annotated set of 153,841 videos having five quality
ratings each, and 1,596 videos with a minimum of 89 ratings each. Additionally,
we propose new efficient VQA approaches (MLSP-VQA) relying on multi-level
spatially pooled deep-features (MLSP). They are exceptionally well suited for
training at scale, compared to deep transfer learning approaches. Our best
method, MLSP-VQA-FF, improves the Spearman rank-order correlation coefficient
(SRCC) performance metric on the commonly used KoNViD-1k in-the-wild benchmark
dataset to 0.82. It surpasses the best existing deep-learning model (0.80 SRCC)
and hand-crafted feature-based method (0.78 SRCC). We further investigate how
alternative approaches perform under different levels of label noise, and
dataset size, showing that MLSP-VQA-FF is the overall best method for videos
in-the-wild. Finally, we show that the MLSP-VQA models trained on KonVid-150k
sets the new state-of-the-art for cross-test performance on KoNViD-1k,
LIVE-VQC, and LIVE-Qualcomm with a 0.83, 0.75, and 0.64 SRCC, respectively. For
both KoNViD-1k and LIVE-VQC this inter-dataset testing outperforms
intra-dataset experiments, showing excellent generalization.
