An efficient six-parameter perspective motion model for VVC |
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Affiliation: | 1. School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran;2. School of Computer Science and Electronic Engineering, University of Essex, Colchester, UK;1. Department of Electronics and Information Engineering, Changchun University of Science and Technology, Jilin 130022, China;2. Departamento de Ó ptica, Facultad de Física, Universidad Complutense, 28040 Madrid, Spain;3. Zhejiang Saisi Electronic Technology Co., Ltd., Jiaxing Science and Technology City, Zhejiang Province 314000, China |
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Abstract: | Tilt and pan camera movements are common in computer games or social media videos. These types of videos contain numerous perspective transforms while today’s video codecs rely on translational and affine motion models for motion compensation. The general perspective motion model with 8 parameters (8PMM) has unreasonably high processing time. In this paper, the eight-parameter perspective transform is simplified into a six-parameter transform to keep the time complexity within an acceptable range while modeling the most relevant transforms. Also, two motion prediction modes, Advanced Perspective Motion Vector Prediction (APMVP) and Perspective Model Merge (PMM), are proposed. The implementation results show an average of 7.0% BD-rate reduction over H.266/VVC Test Model with a maximum of 20% encoding time overhead. The results also show a 71% processing time reduction in comparison to 8PMM while experiencing an average of 5.6% increase in BD-Rate. Much better visual quality is measured through VMAF quality meter. |
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Keywords: | Multimedia streaming Video coding Motion estimation Spatial transform BD-rate reduction |
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