Selective Optical Flow Refinement for Affine Coded Block Prediction
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Solution Overview
Problem
Existing video coding methods using sub-block based affine motion compensation suffer from a loss of prediction accuracy due to high complexity, necessitating a better trade-off between coding complexity and prediction accuracy.
Innovation Solution
Implementing prediction refinement with optical flow (PROF) for affine coded blocks, which refines prediction sample values at a pixel/sample level granularity, only when necessary, to improve accuracy without increasing unnecessary computation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If sub-block based affine motion compensation is used, then coding complexity is reduced, but prediction accuracy deteriorates
Solution Approach 1:
The patent applies different processing qualities to different regions: whole-block affine motion compensation is applied to significant regions (edges, corners) while sub-block based compensation is applied to less critical regions. This local differentiation maintains high prediction accuracy where needed while reducing overall coding complexity.
Solution Approach 2:
The coding block is divided into multiple sub-blocks, and the patent selectively applies affine motion compensation to specific sub-blocks based on their importance (edges, corners, center) rather than uniformly applying it to all sub-blocks. This segmentation strategy reduces the total number of computations while maintaining accuracy in critical areas.
2Measurement precision
If whole-block affine motion compensation is used, then prediction accuracy is improved, but coding complexity increases
Solution Approach 1:
Instead of applying affine motion compensation to all sub-blocks (excessive action), the patent applies it only to necessary sub-blocks such as edges and corners (partial action). This selective application achieves sufficient prediction accuracy without the full computational cost of whole-block compensation.
Solution Approach 2:
The patent changes the parameter of affine model application from binary (apply to all or none) to selective (apply to specific sub-blocks based on their characteristics). By modifying which sub-blocks receive affine compensation based on their position and importance, the system optimizes the trade-off between accuracy and complexity.
Data Source
AI summary
The present disclosure relates to an apparatus, an encoder, a decoder and corresponding methods for prediction refinement with optical flow (PROF) for an affine coded block, in which when a plurality of optical flow decision conditions are fulfilled for the affine coded block, performing a PROF process for a current sub-block of the affine coded block to obtain refined prediction sample values of the current sub-block of the affine coded block. After the sub-block based affine motion compensation is performed, a prediction sample value of the current sample of the current sub-block is refined by adding a delta prediction value. Thus, it allows for a better trade-off between coding complexity and prediction accuracy.