Sub-Block Motion Vector Derivation Using Regression-Based Affine Prediction
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Solution Overview
Problem
Existing video coding standards face challenges in efficiently deriving motion vectors, particularly in handling complex motions and small block sizes, leading to inefficiencies in bandwidth usage and computational complexity.
Innovation Solution
A regression-based motion vector field (RMVF) method is employed to derive motion vectors on a sub-block level using spatially neighboring motion vectors, applying a 6-parameter motion model to improve motion vector prediction accuracy and reduce computational complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing video coding standards are used to derive motion vectors, then coding efficiency is maintained, but bandwidth usage increases and computational complexity increases
Solution Approach 1:
The current video block is divided into multiple sub-blocks, and motion vectors are derived independently for each sub-block using regression-based motion vector field. This segmentation allows for more precise local motion representation while reducing the overall computational burden by processing smaller regions separately.
Solution Approach 2:
Different motion models are applied to different regions of the video block based on local motion characteristics. The regression-based approach adapts the motion vector field to local patterns, providing higher prediction accuracy for regions with complex or small-block motions without requiring complex global models.
2Measurement precision
If existing video coding standards are used to derive motion vectors, then coding efficiency is maintained, but bandwidth demand increases
Solution Approach 1:
Motion vectors for sub-blocks are predicted in advance using regression-based methods from previously decoded neighboring blocks. This preliminary derivation reduces the need to transmit full motion vector information for each block, thereby reducing bandwidth consumption while maintaining accurate motion compensation.
3Productivity
If regression-based motion vector field method is used, then motion vector prediction efficiency is enhanced and bandwidth demand is reduced, but device complexity increases
Solution Approach 1:
The system uses previously decoded motion information from neighboring blocks to automatically derive motion vectors for current blocks through regression analysis. This self-service approach eliminates the need for explicit transmission of motion vector data, improving prediction efficiency while the computational overhead is managed through efficient regression algorithms.
Data Source
AI summary
Devices, systems, and methods for digital video coding, which includes deriving regression-based motion vector field (RMVF)-based motion vector information, are described. An exemplary method for video processing includes deriving, from at least one set of neighboring blocks in a RMVF scheme, one or more control point motion vector predictors (CPMVPs) of an affine model for a current video block. The method also includes updating a motion candidate list for the current video block based on the one or more CPMVPs. The one or more CPMVPs are associated with a specific affine motion mode. The method further includes performing a conversion for the current video block based on the motion candidate list.