Video Processing Motion Vector Prediction Sorting
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Solution Overview
Problem
Conventional video coding techniques have low coding efficiency due to ineffective motion vector prediction (MVP) methods, which fail to exploit prior information and limit diversity in MVP lists, leading to mismatches between true motion information and constructed candidates.
Innovation Solution
The proposed method sorts motion vector prediction candidates based on template matching costs to improve the selection of MVP candidates, enhancing coding effectiveness and efficiency by prioritizing candidates with lower matching costs and increasing diversity within the MVP list.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional MVP methods are used without sorting based on template matching costs, then the construction process is simple, but coding efficiency is low and MVP candidates may not accurately represent true motion information
Solution Approach 1:
The patent calculates template matching costs for all MVP candidates in advance before final selection. This preliminary calculation allows the system to sort and select the most appropriate candidates based on their matching costs, ensuring high coding efficiency while maintaining a manageable construction process through systematic pre-processing
Solution Approach 2:
The patent introduces template matching cost as an additional parameter to evaluate and rank MVP candidates. By changing the selection criterion from simple positional or sequential selection to cost-based ranking, the system improves coding efficiency by selecting candidates that better represent true motion information
2Adaptability or versatility
If MVP candidates are selected without considering template matching costs, then the construction process is fast, but the diversity and appropriateness of MVP candidates are limited
Solution Approach 1:
The system performs preliminary template matching cost calculations for all potential MVP candidates before the actual selection process. This advance preparation enables diverse and appropriate candidate selection without significantly increasing construction time, as the costly template matching is done once and reused for ranking multiple candidates
Solution Approach 2:
By introducing template matching cost as a ranking parameter, the system enhances MVP list diversity and appropriateness. The cost-based sorting mechanism ensures that candidates with better motion representation are prioritized, improving the adaptability of the MVP list to various motion scenarios
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, a set of motion vector prediction (MVP) candidates of the target video block based on decoded information of the target video block; sorting the set of MVP candidates based on respective template matching costs of the set of MVP candidates; and performing the conversion based on the sorting.