| Title | Video Quality Assessment Dataset for Smart Public Security Systems |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | Bezzine, Ismail, Khan, Zohaib Amjad, Beghdadi, Azeddine, Al-Maadeed, Noor, Kaaniche, Mounir, Al-Maadeed, Somaya, Bouridane, Ahmed, Cheikh, Faouzi Alaya |
| Conference Name | 2020 IEEE 23rd International Multitopic Conference (INMIC) |
| Date Published | Nov. 2020 |
| Publisher | IEEE |
| ISBN Number | 978-1-7281-9893-4 |
| Keywords | Databases, distortion, distortions generationm subjective tests, lighting, Measurement, Metrics, metrics testing, pubcrawl, quality assessment, video recording, video surveillance |
| Abstract | Security and monitoring systems are more and more demanding in terms of quality, reliability and flexibility especially those dedicated to video surveillance. The quality of the acquired video signal strongly affects the performance of the high level tasks such as visual tracking, face detection and recognition. The design of a video quality assessment metric dedicated to this particular application requires a preliminary study on the common distortions encountered in video surveillance. To this end, we present in this paper a dataset dedicated to video quality assessment in the context of video surveillance. This database consists of a set of common distortions at different levels of annoyance. The subjective tests are performed using a classical pair comparison protocol with some new configurations. The subjective results obtained through the psycho-visual tests are analyzed and compared to some objective video quality assessment metrics. The preliminary results are encouraging and open a new framework for building smart video surveillance based security systems. |
| URL | https://ieeexplore.ieee.org/document/9318149/ |
| DOI | 10.1109/INMIC50486.2020.9318149 |
| Citation Key | bezzine_video_2020 |
Video Quality Assessment Dataset for Smart Public Security Systems