Video Processing MVP List Construction Using Template Matching
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Solution Overview
Problem
Conventional video coding techniques have low coding efficiency due to limitations in motion vector prediction (MVP) list construction, which fails to exploit prior information and diversity in motion information, leading to mismatches and reduced effectiveness.
Innovation Solution
The proposed method constructs an optimized MVP list using template matching costs and pruning thresholds to select more appropriate candidates, incorporating non-adjacent temporal motion vector predictions and prioritizing candidates based on their consistency with true motion information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional MVP list construction is used, then device complexity is reduced, but coding efficiency deteriorates due to inability to exploit prior information and motion information diversity
Solution Approach 1:
The patent performs preliminary actions by calculating template matching costs for multiple MVP candidates before final selection. The method pre-processes motion information from neighboring blocks and temporal reference blocks, constructing an enriched MVP candidate list in advance with diversity considerations, rather than simply selecting candidates during the main encoding process.
Solution Approach 2:
The patent changes the selection parameter from simple spatial/temporal proximity to template matching cost. By introducing template matching cost calculation as the selection criterion, the method enables more accurate motion prediction while maintaining manageable complexity through efficient cost computation algorithms.
2Measurement precision
If template matching cost calculation is performed for all MVP candidates, then motion prediction accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent applies local quality by performing template matching cost calculation selectively on specific MVP candidates rather than uniformly on all candidates. The method focuses computational resources on candidates from regions with high motion activity or those that show promise based on preliminary analysis, thereby improving accuracy where needed while controlling overall complexity.
Data Source
AI summary
Embodiments of the present disclosure provide a solution for video processing. A method for video processing is proposed. The method comprises: determining, during a conversion between a target video block of a video and a bitstream of the video, respective template matching costs of a plurality of motion vector prediction (MVP) candidates of the target video block; determining a MVP candidate list based on the respective template matching costs; and performing the conversion based on the MVP candidate list.