Abstract
During lossy video compression, a blocking artifact is introduced into a video. It can significantly reduce the quality of a compressed video, and consequently end-user Quality of Experience. Hence, it is very important to measure and monitor the quality of compressed videos delivered to the end-user, which can be performed only by using the no-reference (NR) approach, without the original video signal available. Many NR video quality assessment (VQA) metrics are based on measurements of different artifacts visibility, which are then used as an input for an objective NR VQA metric. Thus, reliable artifacts detection and their visibility estimation are the first step in a reliable VQA process. In this paper, a new algorithm for Real-Time NR Blocking Visibility Estimation (RT-BVE) in video frames is proposed. The performance of the proposed RT-BVE algorithm is compared to the performance of two freely available algorithms, i.e., BBT-BDA and MSU Blocking, using videos from the well-known databases (156 video sequences in total and more than 48,000 frames). While RT-BVE achieves performance comparable to BBT-BDA and MSU Blocking algorithms for MPEG-2 videos, it outperforms both algorithms for H.264 videos. The results show that the proposed RT-BVE algorithm can precisely estimate blocking visibility in video frames for videos compressed according to different compression standards by using a fixed set of algorithm parameter values. Additionally, RT-BVE is suitable for usage in real-time applications since it is capable of processing approximately 100 Full HD video frames per second on the current midrange × 86–64 platform.
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Acknowledgements
This work was supported by J.J. Strossmayer University of Osijek through the internal competition for research and artistic projects via the project “UNIOS-ZUP 2018-6.” The authors would like to thank the VQMT support team for providing the results for the MSU Quality Measurement Tool.
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Jurić, T., Vranješ, M., Grbić, R. et al. RT-BVE—Real-Time no-reference Blocking Visibility Estimation in video frames. J Real-Time Image Proc 18, 1921–1936 (2021). https://doi.org/10.1007/s11554-020-01065-9
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DOI: https://doi.org/10.1007/s11554-020-01065-9