Multi-Model Motion Prediction for 360-Degree Video Compression

Regensky A, Herglotz C, Kaup A (2023)


Publication Type: Journal article

Publication year: 2023

Journal

Book Volume: 11

Pages Range: 117004-117017

DOI: 10.1109/ACCESS.2023.3326717

Abstract

Efficient video compression is fundamental for enabling today’s highly interactive multimedia landscape. With the trend towards virtual reality, efficient storage and transmission of 360-degree video content becomes increasingly important. Recent works in this area addressed the design and investigation of 360-degree-specific motion models for improved compression efficiency. We investigate the strengths and limitations of these motion models and show that a single model cannot sufficiently cover all motion scenarios. A video codec that combines the strengths of multiple models in the motion compensation procedure is expected to achieve notable gains in compression efficiency. However, the additional motion model signaling and switching costs quickly outweigh the gains achieved by improved motion compensation. In this paper, we address this challenge by proposing advanced motion prediction and coding schemes that significantly reduce the resulting side information overhead. A novel multi-model motion vector prediction technique ensures seamless cooperation between different motion models by generalizing the motion modeling concept through forward and backward passes, and yields significant improvements in the overall compression efficiency. Our proposed hierarchical coding scheme is broadly applicable and is shown to be the most effective among comparable coding schemes. Experimental results demonstrate the performance of the proposed multi-model coding framework with average Bjøntegaard Delta rate savings of 3.20% with a peak of 4.49% based on PSNR and 2.76% with a peak of 4.04% based on WS-PSNR compared to the state-of-the-art H.266/VVC video coding standard.

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How to cite

APA:

Regensky, A., Herglotz, C., & Kaup, A. (2023). Multi-Model Motion Prediction for 360-Degree Video Compression. IEEE Access, 11, 117004-117017. https://doi.org/10.1109/ACCESS.2023.3326717

MLA:

Regensky, Andy, Christian Herglotz, and André Kaup. "Multi-Model Motion Prediction for 360-Degree Video Compression." IEEE Access 11 (2023): 117004-117017.

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