  The proliferation of in-the-wild videos has greatly expanded the Video
Quality Assessment (VQA) problem. Unlike early definitions that usually focus
on limited distortion types, VQA on in-the-wild videos is especially
challenging as it could be affected by complicated factors, including various
distortions and diverse contents. Though subjective studies have collected
overall quality scores for these videos, how the abstract quality scores relate
with specific factors is still obscure, hindering VQA methods from more
concrete quality evaluations (e.g. sharpness of a video). To solve this
problem, we collect over two million opinions on 4,543 in-the-wild videos on 13
dimensions of quality-related factors, including in-capture authentic
distortions (e.g. motion blur, noise, flicker), errors introduced by
compression and transmission, and higher-level experiences on semantic contents
and aesthetic issues (e.g. composition, camera trajectory), to establish the
multi-dimensional Maxwell database. Specifically, we ask the subjects to label
among a positive, a negative, and a neutral choice for each dimension. These
explanation-level opinions allow us to measure the relationships between
specific quality factors and abstract subjective quality ratings, and to
benchmark different categories of VQA algorithms on each dimension, so as to
more comprehensively analyze their strengths and weaknesses. Furthermore, we
propose the MaxVQA, a language-prompted VQA approach that modifies
vision-language foundation model CLIP to better capture important quality
issues as observed in our analyses. The MaxVQA can jointly evaluate various
specific quality factors and final quality scores with state-of-the-art
accuracy on all dimensions, and superb generalization ability on existing
datasets. Code and data available at https://github.com/VQAssessment/MaxVQA.
