Abstract
In order to assess video quality more accurately, this paper proposes an improved video quality assessment algorithm based on persistence-of-vision effect. This algorithm firstly adopts region partitioning, Just Noticeable Difference (JND) model, etc. to assess the quality of a video single frame; then conducts perceptual weighting on the several affected frames based on persistence-of-vision effect when the video scene changes; and finally assesses the video quality by using linear correlation coefficient and Peirman correlation coefficient and compares with the performance of the traditional algorithm through experiments. The experimental results show that the proposed algorithm in this paper can objectively describe the video quality and perform well in the materials with more radical scene changes.
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Liu, P., Liu, F., Gong, D. (2018). Video Quality Assessment Algorithm Based on Persistence-of-Vision Effect. In: Sun, X., Pan, Z., Bertino, E. (eds) Cloud Computing and Security. ICCCS 2018. Lecture Notes in Computer Science(), vol 11064. Springer, Cham. https://doi.org/10.1007/978-3-030-00009-7_34
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DOI: https://doi.org/10.1007/978-3-030-00009-7_34
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