Video Processing MVP Candidate List Reordering
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Solution Overview
Problem
Conventional video coding techniques have low coding efficiency due to inadequate motion vector prediction (MVP) candidate list construction, which affects the overall compression and decoding processes.
Innovation Solution
The proposed method involves determining multiple MVP candidate lists through reordering and pruning processes, limiting the number of candidates, and using threshold-based approaches to construct more appropriate MVP lists, thereby improving coding effectiveness and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional video coding techniques are used, then the implementation is simple, but the coding efficiency is low
Solution Approach 1:
The MVP candidate list construction is divided into multiple passes: a first pass that generates an initial candidate list, and a second pass that refines and reorders the candidates. This segmentation allows systematic improvement of coding efficiency while managing complexity through structured processing stages.
Solution Approach 2:
The first pass performs preliminary selection and ordering of MVP candidates before the second pass refines them further. This preliminary action prepares the candidate list in advance, enabling the second pass to focus on optimization rather than initial construction, thereby improving overall coding efficiency.
2Manufacturing precision
If a single pass of reordering is used in MVP candidate list construction, then the processing is faster, but the coding effectiveness is insufficient
Solution Approach 1:
The MVP candidate list construction uses periodic action through multiple passes: the first pass performs initial reordering, and the second pass performs further reordering and refinement. This periodic multi-pass approach improves coding effectiveness by systematically enhancing candidate quality at each stage while managing processing time through structured iterations.
3Adaptability or versatility
If the number of MVP candidates is not limited, then more candidates are available for selection, but the processing complexity increases
Solution Approach 1:
The patent applies parameter changes by imposing a threshold number limit on MVP candidates in the second pass. This parameter constraint reduces processing complexity while maintaining adaptability, as the system still selects from multiple candidates within the defined limit, balancing versatility with manageable 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 be-tween a target video block of a video and a bitstream of the video, at least one group of motion vector prediction (MVP) candidates of the target video block; determining a first MVP candidate list by performing a first pass of reordering process on the at least one group of MVP candidates; determining a second MVP candidate list by performing a second pass of reordering process on the first MVP candidate list; and performing the conversion based on the second MVP candidate list. In this way, a proper MVP candidate list can be determined by using the first and second passes of reordering, and thus the coding effectiveness and coding efficiency can be improved.