DMVR Motion Vector Refinement for Video Compression
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current video coding techniques face challenges in efficiently compressing and decompressing video data, particularly in reducing the complexity of prediction processes while maintaining high compression ratios and picture quality, especially when dealing with limited bandwidth and storage resources.
Innovation Solution
The method involves obtaining an initial motion vector and reference picture for bi-prediction, deriving candidate sample positions using motion vector offsets, computing matching costs, and refining the motion vector to improve prediction accuracy, thereby reducing the complexity of the prediction process and enhancing compression efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If motion vector refinement process is used to improve initial motion vector accuracy, then motion vector accuracy is improved, but computational complexity increases
Solution Approach 1:
The prediction block is divided into multiple sub-blocks, and motion vectors are refined independently for each sub-block. This segmentation allows the refinement process to focus computational resources on smaller regions, improving local motion accuracy while distributing the computational load across multiple smaller operations rather than one large complex operation.
Solution Approach 2:
The patent applies motion vector refinement selectively rather than uniformly across all blocks. By identifying blocks that benefit most from refinement and applying the process only to those cases, the system achieves improved accuracy where needed while avoiding unnecessary computational complexity in blocks where the initial motion vector is already sufficient.
2Quantity of substance
If compression ratio is increased to reduce data size, then bandwidth and storage requirements are reduced, but picture quality deteriorates
Solution Approach 1:
The patent changes the parameter of motion vector precision by applying refinement processes that improve motion vector accuracy beyond the initial coarse estimation. This allows the system to achieve better prediction accuracy without increasing the base compression ratio, thereby maintaining picture quality while still achieving data reduction through standard compression techniques.
Data Source
AI summary
The present disclosure provides an inter prediction method, comprising the steps of obtaining an initial motion vector and a reference picture for bi-prediction; obtaining sets of candidate sample positions in the reference picture according to the initial motion vector and candidate motion vectors, wherein each candidate motion vector is derived by the initial motion vector and a respective motion vector offset, and wherein each set of candidate sample positions corresponds to each candidate motion vector; obtaining a respective set of sample positions from each set of candidate sample positions; computing a matching cost for each candidate motion vector within each set of sample positions; obtaining a refined motion vector based on the computed matching cost of each candidate motion vector; and obtaining prediction values for a current block based on the refined motion vector.