Videos captured with body-worn cameras
may be affected by distortions such as motion blur,
overexposure and reduced contrast. Automated video
quality assessment is therefore important prior to
auto-tagging, event or object recognition, or automated
editing. In this paper, we present M-BRISQUE,
a spatial quality evaluator that combines, in realtime,
the Michelson contrast with features from the
Blind/Referenceless Image Spatial QUality Evaluator.
To link the resulting quality score to human judgement,
we train a Support Vector Regressor with Radial Basis
Function kernel on the Computational and Subjective
Image Quality database. We show an example of application
of M-BRISQUE in automatic editing of multicamera
content using relative view quality, and validate
its predictive performance with a subjective evaluation
and two public datasets.
Real-time quality assessment of videos from body-worn cameras
Yuan-Yi Chang, Riccardo Mazzon, Andrea Cavallaro
EUSIPCO 2018 (to appear)