Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6941550 | Signal Processing: Image Communication | 2018 | 15 Pages |
Abstract
In this paper, we propose a new video quality metric based on a set of multiple features that incorporate texture, saliency, spatial activity, and temporal attributes. A random forest regression algorithm is used to combine these features and obtain a video quality score. Experimental results show that the proposed metric has a good performance when tested on several benchmark video quality databases, outperforming current state-of-the-art full-reference video quality metrics.
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Vision and Pattern Recognition
Authors
Pedro Garcia Freitas, Welington Y.L. Akamine, Mylène C.Q. Farias,