Hybrid Motion Vector Prediction for Complex Bi-Predicted Blocks
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Solution Overview
Problem
Conventional video coding methods fail to fully leverage the complementary nature of spatial and temporal motion vectors, leading to sub-optimal motion prediction, particularly in scenarios with complex motion patterns or occlusions.
Innovation Solution
A hybrid spatial-temporal motion vector prediction framework that combines motion vectors from both spatial and temporal domains to generate a more accurate and efficient motion vector predictor, incorporating subblock-level granularity and advanced techniques like chained motion vector prediction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional spatial or temporal motion vector prediction is used separately, then the implementation is simple, but the motion prediction accuracy is insufficient for complex motion patterns
Solution Approach 1:
The patent combines spatial motion vectors (from neighboring blocks in the same picture) and temporal motion vectors (from co-located blocks in reference pictures) into a unified hybrid motion vector predictor. This merging allows the system to leverage both spatial and temporal correlations simultaneously, improving motion prediction accuracy for complex motion patterns while maintaining a manageable implementation complexity through systematic integration of the two vector types.
2Measurement precision
If hybrid spatial-temporal motion vector prediction is implemented, then motion prediction accuracy improves, but computational complexity increases
Solution Approach 1:
The patent segments the current block into multiple subblocks and performs hybrid spatial-temporal motion vector prediction at the subblock level. This segmentation allows the computational complexity to be distributed across smaller units, making the overall process more manageable while achieving higher prediction accuracy through fine-grained motion compensation that adapts to local motion variations within the block.
3Measurement precision
If motion vectors are derived at block level only, then the processing is efficient, but the granularity is too coarse for complex motion patterns
Solution Approach 1:
The patent implements a dynamic approach by allowing different prediction modes (spatial, temporal, and their combinations) to be selected and applied at different subblock levels based on local motion characteristics. This dynamic adaptation enables the system to achieve fine granularity where needed for complex motion patterns while maintaining coding efficiency through selective application of the more computationally intensive methods only where necessary.
Data Source
AI summary
A video coder receives a block to be decoded using bi-prediction, determines a spatial motion vector for the block, determines an additional motion vector based on the spatial motion vector, wherein the additional motion vector is a temporal motion vector or a chained motion vector predictor (CMVP), generates a hybrid spatial-temporal motion vector based on the spatial motion vector and the additional motion vector, and codes the block of video data using bi-prediction and the hybrid spatial-temporal motion vector.