Geometric Partition Motion Vector Refinement for Lower Signaling Overhead
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Solution Overview
Problem
Existing video coding standards, such as VVC and AVS3, face inefficiencies in the geometric partition mode (GPM) due to suboptimal motion vector refinement, particularly for non-merge inter modes, leading to increased signaling overhead and reduced coding efficiency.
Innovation Solution
Implementing geometric partition mode with motion vector refinement (GPM-MVR) to enhance motion accuracy for GPM partitions by applying predefined motion refinements similar to the MMVD design, minimizing signaling costs while improving motion vector precision.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If motion vector refinement is applied to GPM partitions, then motion accuracy is improved, but signaling overhead increases
Solution Approach 1:
The video block is divided into two geometric partitions with different motion characteristics. Each partition can independently apply motion vector refinement techniques (such as TMVD or MMVD) to improve motion accuracy locally without requiring full refinement across the entire block, thus balancing accuracy improvement with signaling overhead control.
Solution Approach 2:
Motion vector refinement is applied selectively rather than universally. The patent allows the decoder to choose whether to apply refinement techniques like TMVD or MMVD based on mode flags, enabling partial refinement only where needed to improve accuracy while avoiding unnecessary signaling overhead in regions where refinement is not beneficial.
2Adaptability or versatility
If geometric partition mode is used for non-merge inter modes, then coding flexibility is improved, but signaling overhead increases
Solution Approach 1:
The patent merges the advantages of geometric partition mode with existing merge modes (AMVP and merge). By integrating GPM into the merge framework and allowing selection through mode flags, the system achieves coding flexibility similar to non-merge modes while utilizing the efficient merge candidate structure to control signaling overhead.
Solution Approach 2:
The geometric partition mode is designed to work universally across different inter prediction modes (AMVP, merge, and skip). The same GPM structure and partitioning approach can be applied regardless of the base mode, providing coding flexibility through a single versatile mechanism rather than requiring separate specialized modes for each inter prediction type.
Data Source
AI summary
Methods and devices are provided for decoding a video block in GPM. The method includes: partitioning the video block into first and second geometric partitions; receiving a first GPM with a motion vector refinement (GPM-MVR) enable flag for the first geometric partition and receiving a second GPM-MVR enable flag for the second geometric partition; receiving a joint template matching (TM) enable flag for the first and second geometric partition that jointly indicates whether a uni-directional motion of the first partition is refined by TM and whether a uni-directional motion of the second partition is refined by the TM; receiving a first merge GPM index for the first geometric partition and a second merge GPM index for the second geometric partition; constructing a uni-directional MV candidate list of the GPM; and generating a uni-directional MV for the first geometric partition and a uni-directional MV for the second geometric partition.


