@InProceedings{8553612,
  author = {Y. Chang and R. Mazzon and A. Cavallaro},
  booktitle = {2018 26th European Signal Processing Conference (EUSIPCO)},
  title = {Real-Time Quality Assessment of Videos from Body-Worn Cameras},
  year = {2018},
  pages = {2160-2164},
  abstract = {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 multi-camera content using relative view quality, and validate its predictive performance with a subjective evaluation and two public datasets.},
  keywords = {cameras;image capture;image motion analysis;image restoration;interference (signal);object recognition;radial basis function networks;regression analysis;support vector machines;video signal processing;Radial Basis Function;multicamera content;event recognition;object recognition;auto-tagging;support vector regressor;computational image quality database;subjective image quality database;Blind/Referenceless Image Spatial QUality Evaluator;Michelson contrast;M-BRISQUE;automated editing;automated video quality assessment;body-worn cameras;Videos;Distortion;Cameras;Quality assessment;Databases;Real-time systems;Body-worn cameras;video quality;real-time processing},
  doi = {10.23919/EUSIPCO.2018.8553612},
  issn = {2076-1465},
  month = {Sep.},
  url = {https://www.eurasip.org/proceedings/eusipco/eusipco2018/papers/1570437924.pdf},
}

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