Affine Prediction Candidate Selection for Video Block Coding

The prediction candidate list is segmented into multiple types (spatial candidates, temporal candidates, combined candidates) with different selection criteria and priority levels. This segmentation allows the system to process different candidate types through specialized selection paths, improving overall coding efficiency while maintaining manageable complexity through organized processing stages.

The prediction list construction uses dynamic selection mechanisms where candidate priorities and selection criteria adapt based on block characteristics, motion complexity, and available reference data. This dynamic approach enables the system to optimize coding efficiency for different video content types without requiring a completely different processing architecture for each case.

The system applies advanced prediction methods selectively rather than uniformly across all video blocks. By identifying blocks that benefit most from complex prediction (e.g., high-motion regions, texture-rich areas) and applying simplified methods to other blocks, the system achieves improved coding efficiency where needed while controlling overall computational power consumption.

The prediction process dynamically adjusts parameters such as search range, candidate list size, and refinement iterations based on block characteristics and encoding conditions. This parameter adaptation allows the system to achieve high coding efficiency when necessary while reducing computational power consumption during low-complexity encoding scenarios.