用仿射运动模型提升360度视频压缩质量，显著降低码率。
Improved Motion Plane Adaptive 360-Degree Video Compression Using Affine Motion Models
- 引入仿射参数化改进运动补偿，更精准描述复杂运动。
- 相比传统MPA，重建画质提升约1.6 dB（WS-PSNR）。
- 适合需要高效360度视频编码的系统开发者或研究人员。
360度视频高效压缩需依赖先进的帧间预测运动模型。运动平面自适应（MPA）模型将帧投影至三维空间中的多个视角平面，并通过平移钻石搜索估计各平面上的运动。本文在此基础上引入仿射参数化及相应的运动估计算法，实现重构质量与计算开销间的良好权衡。仿射运动估计采用逆复合Lucas-Kanade算法。所提方法显著改善运动补偿效果，使运动补偿后帧的加权到球面均匀峰值信噪比（WS-PSNR）相较传统MPA提升约1.6 dB。在基础视频编码器中，该改进可带来9%至35%的贝约恩特加德率节省（BD-rate），具体取决于块大小（BS）和运动参数数量。
原文摘要 · Abstract (English)
Efficient compression of 360-degree video content requires the application of advanced motion models for interframe prediction. The Motion Plane Adaptive (MPA) motion model projects the frames on multiple perspective planes in the 3D space. It improves the motion compensation by estimating the motion on those planes with a translational diamond search. In this work, we enhance this motion model with an affine parameterization and motion estimation method. Thereby, we find a feasible trade-off between the quality of the reconstructed frames and the computational cost. The affine motion estimation is hereby done with the inverse compositional Lucas-Kanade algorithm. With the proposed method, it is possible to improve the motion compensation significantly, so that the motion compensated frame has a Weighted-to-Spherically-uniform Peak Signal-to-Noise Ratio (WS-PSNR) which is about 1.6 dB higher than with the conventional MPA. In a basic video codec, the improved inter prediction can lead to Bjøntegaard Delta (BD) rate savings between 9 % and 35 % depending on the block size (BS) and number of motion parameters.
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