Inter-Frame Motion Vector Prediction Using Offset Template Matching
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Solution Overview
Problem
Existing inter-frame prediction methods suffer from insufficient prediction accuracy due to inadequate selection of motion vector prediction candidates, leading to inefficient video compression.
Innovation Solution
Select multiple motion vector prediction candidates with minimal error between current and reference templates, perform offset processing, and use the resulting candidates as search start points for template matching to enhance prediction accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If only one motion vector prediction candidate with minimum error is selected for template matching, then the computational complexity is reduced, but the prediction accuracy is insufficient
Solution Approach 1:
The motion vector prediction process is segmented into multiple stages: first selecting multiple candidates from the candidate list based on error ranking, then performing offset processing to generate additional candidates, and finally conducting template matching on all generated candidates. This segmentation allows comprehensive candidate evaluation while organizing computation in manageable stages.
Solution Approach 2:
Offset processing is performed preliminarily on the selected motion vector prediction candidates before template matching. This preliminary action generates offset motion vector prediction candidates that expand the search space, ensuring that the subsequent template matching operates on a more comprehensive set of candidates, thereby improving prediction accuracy without requiring exhaustive search.
2Measurement precision
If multiple motion vector prediction candidates are selected and processed with offset, then the prediction accuracy is improved, but the computational complexity increases
Solution Approach 1:
The method applies different processing operations to different candidates based on their error ranking. Candidates with lower error values undergo offset processing to generate additional candidates, while candidates with higher errors are excluded. This local quality approach ensures computational resources are focused on the most promising candidates, improving prediction accuracy while controlling complexity.
Solution Approach 2:
The method changes the parameter of candidate quantity dynamically. Instead of using a fixed number of candidates, the system selects multiple candidates based on error ranking criteria and generates additional candidates through offset processing. This parameter change allows adaptive control of computational complexity based on the specific video content and motion characteristics.
Data Source
AI summary
An inter-frame prediction method, a device, and a readable storage medium are provided. The method includes: determining a plurality of motion vector prediction candidates from a motion vector prediction candidate list of a target codec unit according to a template; performing offset processing on the plurality of motion vector prediction candidates to obtain a plurality of offset motion vector prediction candidates; determining a target motion vector prediction candidate according to the plurality of offset motion vector prediction candidates; and using the target motion vector prediction candidate as a search start point to obtain a target motion vector prediction through template matching, where the plurality of motion vector prediction candidates are a plurality of motion vector prediction candidates whose error between a current template and a reference template in the motion vector prediction candidate list is ranked in a first preset position.