Accelerating Merge with Motion Vector Difference via Filter Difference Analysis for VVenC

2026-06-29

AI Summary

This work addresses the high computational cost of motion vector refinement with merge mode (MMVD) in the VVenC encoder, which stems from exhaustive search. To mitigate this, an efficient candidate evaluation method is proposed, leveraging differential analysis of fractional-pixel interpolation filters to derive a fast assessment criterion that integrates spatial gradients and prediction residuals. The approach is generalized to integer offsets and two-dimensional separable filtering scenarios. Key innovations include a 2-tap approximation of the standard 8-tap filter, symmetric offset inference, and cross-shaped downsampled dot-product optimization, collectively reducing complexity substantially. Experimental results under the VVenC fast preset demonstrate that the average MMVD search ratio decreases from 21.07% to 11.05%, while the efficiency-complexity metric η improves from 11.79 to 7.10.

Abstract

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.

Problem

• MMVD
• computational complexity
• motion prediction
• VVenC
• video coding

Innovation

• MMVD
• filter difference analysis
• fast algorithm
• motion vector
• VVenC