Bidirectional Weight Index Derivation in Affine Merge Video Decoding

The patent applies parameter changes by modifying the prediction weight derivation process. Instead of using fixed or simple adaptive weight methods, the patent dynamically determines prediction weights based on multiple factors including prediction direction, reference picture type, and motion characteristics. This allows the system to adapt the complexity of weight derivation to the specific characteristics of each video block, improving compression efficiency without uniformly increasing complexity across all blocks.

The patent introduces dynamic adaptation in the prediction weight derivation process. The prediction weights are not static but are dynamically calculated based on the specific characteristics of each block, including its motion direction, reference picture selection, and temporal-spatial relationships. This dynamic approach allows the encoder to focus computational resources on blocks that benefit most from complex weight derivation, thereby improving overall efficiency without proportionally increasing complexity.

The patent applies local quality by deriving prediction weights specifically for blocks that exhibit complex motion characteristics, rather than applying uniform complex processing to all blocks. The method identifies blocks where bidirectional prediction with adaptive weights will provide the most benefit (such as blocks with significant motion or complex temporal-spatial relationships) and applies enhanced weight derivation only to those regions. This localized approach improves image quality for problematic blocks while minimizing the overall encoding time impact.

The patent implements partial action by selectively applying complex bidirectional prediction weight derivation only when necessary, rather than universally. The encoder assesses each block's characteristics and applies the more computationally intensive weight derivation process only to blocks where it will provide measurable quality improvement. For blocks with simple motion characteristics, simpler prediction methods are used, thus reducing overall encoding time while maintaining quality where it matters most.

The patent applies segmentation by dividing the prediction parameter derivation process into distinct stages and components. Instead of calculating all prediction weights simultaneously in a complex monolithic process, the method segments the derivation into separate steps: determining reference picture weights, calculating temporal-spatial relationships, adjusting for motion characteristics, and finalizing bidirectional weights. This segmented approach makes the overall complex process more manageable and implementable while maintaining high prediction accuracy.

The patent applies preliminary action by pre-calculating and storing certain prediction parameters before the actual prediction process. Reference picture weights, motion characteristics, and temporal-spatial relationship factors are determined in advance and cached for use during decoding. This preliminary preparation reduces the computational burden during real-time prediction, allowing the system to maintain high prediction accuracy without proportionally increasing real-time processing complexity.