通过滤波器差分分析，加速视频编码中运动向量合并的搜索过程。
Accelerating Merge with Motion Vector Difference via Filter Difference Analysis for VVenC
- 用2抽头滤波器近似8抽头滤波器，结合空间梯度和预测残差估计候选收益。
- 将平均搜索比例从21.07%降至11.05%，效率复杂度指标η由11.79降至7.10。
- 适合追求编码效率与计算开销平衡的视频压缩系统开发者使用。
运动向量差分合并（MMVD）是通用视频编码（VVenC）中提升运动预测精度的关键工具，但其全搜索策略给编码器带来显著计算负担。本文提出一种基于分数运动向量滤波器差分分析的快速MMVD算法。通过将8抽头插值滤波器近似为2抽头滤波器，推导出基于空间梯度和预测残差的候选增益估计准则，并进一步推广至支持移位整数参考样本和二维可分离滤波。为降低方法开销，引入对称偏移推断与十字形下采样点积计算等实现优化。在快速编码配置下，相比现有VVenC快速MMVD算法，本方法将平均搜索比例从21.07%降至11.05%，效率-复杂度指标η由11.79降至7.10。
原文摘要 · Abstract (English)
Merge with Motion Vector Difference (MMVD) is a key coding tool in Versatile Video Coding for improving motion prediction accuracy. However, its exhaustive search strategy imposes a significant computational burden on the encoder. To address this issue, we propose a novel fast MMVD algorithm for the VVenC encoder based on fractional motion vector filter difference analysis. By approximating the 8-tap interpolation filter with a 2-tap filter, we derive a criterion based on spatial gradients and prediction residuals for estimating the potential gain of MMVD candidates. We further generalize this criterion to accommodate both shifted integer reference samples and 2D separable filtering. To minimize the overhead of the proposed method, we introduce implementation optimizations, including symmetric offset inference and cross-shaped downsampled dot-product computation. Compared with existing fast MMVD algorithms in VVenC, our method reduces the average MMVD search ratio from 21.07\% to 11.05\% and decreases the efficiency-complexity metric $η$ from 11.79 to 7.10 under the fast preset.
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