Recent years have witnessed a rapid growth of user generated videos thanks to the availability of affordable video recording devices and the extreme popularity of social media platforms. Accordingly, there is a challenge for designing highly efficient video quality assessment models to monitor, control, and optimize this content. In this contribution, a novel no-reference video quality metric for user generated video is presented. It exploits the spatial and temporal information contained in the center patch of video frames and a Support Vector Regressor system for computing the objective score. Experimental results show the effectiveness of the proposed approach. To promote reproducible research and public evaluation, an implementation of RM3VQA has been made available online: https://github.com/pramitmazumdar/RM3VQA.
Lamichhane, K., Mazumdar, P., Battisti, F., Carli, M. (2021). A No Reference Deep Learning Based Model for Quality Assessment of UGC Videos. In 2021 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2021 (pp.1-5). Institute of Electrical and Electronics Engineers Inc. [10.1109/ICMEW53276.2021.9456011].
A No Reference Deep Learning Based Model for Quality Assessment of UGC Videos
Lamichhane K.;Mazumdar P.;Carli M.
2021-01-01
Abstract
Recent years have witnessed a rapid growth of user generated videos thanks to the availability of affordable video recording devices and the extreme popularity of social media platforms. Accordingly, there is a challenge for designing highly efficient video quality assessment models to monitor, control, and optimize this content. In this contribution, a novel no-reference video quality metric for user generated video is presented. It exploits the spatial and temporal information contained in the center patch of video frames and a Support Vector Regressor system for computing the objective score. Experimental results show the effectiveness of the proposed approach. To promote reproducible research and public evaluation, an implementation of RM3VQA has been made available online: https://github.com/pramitmazumdar/RM3VQA.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
