Video Motion Vector Candidate Pruning for Coding Efficiency
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Solution Overview
Problem
Existing video coding technologies face challenges in improving coding efficiency and effectiveness due to redundant motion vector prediction (MVP) candidates in the candidate list, which affect the overall coding performance.
Innovation Solution
A method for video processing that involves determining a candidate list of motion vector prediction candidates by applying pruning processes to eliminate redundancy and enhance diversity, thereby improving coding efficiency and effectiveness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple motion vector prediction candidates are generated without pruning, then the candidate list contains more options for prediction, but redundant candidates reduce coding efficiency and effectiveness
Solution Approach 1:
The patent extracts and removes redundant motion vector prediction candidates from the candidate list through pruning processes. By identifying and taking out duplicate or highly similar candidates, the system maintains prediction diversity while eliminating redundancy that harms coding efficiency, directly resolving the contradiction between having many prediction options and maintaining coding productivity
Solution Approach 2:
The patent applies pruning processes that change the composition parameters of the candidate list by selectively removing candidates based on redundancy criteria. This parameter change transforms the candidate list from a potentially redundant set to an optimized set that balances diversity and efficiency, improving coding performance while maintaining prediction versatility
2Adaptability or versatility
If redundant candidates are included in the candidate list, then more prediction options are available, but the coding effectiveness deteriorates
Solution Approach 1:
The patent extracts redundant candidates that harm coding effectiveness while preserving useful prediction options. By selectively removing candidates that duplicate or closely resemble existing entries, the system maintains reliable coding effectiveness while preserving adequate prediction versatility through the remaining diverse candidates
3Productivity
If pruning processes are applied to remove redundant candidates, then coding efficiency improves, but the complexity of the video processing system increases
Solution Approach 1:
The patent segments the candidate list into groups based on motion vector characteristics and applies pruning processes to each segment. This segmentation approach allows systematic removal of redundant candidates while managing processing complexity through structured, manageable operations rather than a single complex filtering step
Solution Approach 2:
The patent performs preliminary organization and grouping of motion vector prediction candidates before applying pruning processes. This preliminary action structures the data to facilitate more efficient pruning operations, reducing the computational complexity of the overall process while maintaining coding efficiency improvements
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, for a conversion between a current video block of a video and a bitstream of the video, a plurality of motion vector prediction (MVP) candidates of the current video block; determining a candidate list of the current video block by applying a plurality of pruning processes to the plurality of MVP candidates; and performing the conversion based on the candidate list.