Mixture-of-Experts Video Compression for Inter/Intra Prediction

The system divides the prediction task into multiple specialized neural network models, each handling different prediction modes (inter-prediction and intra-prediction). Each model is trained separately on specific data distributions, allowing the system to achieve high rate-distortion performance by selecting the appropriate specialized model for each block while managing complexity through modular architecture.

The system dynamically selects which neural network model to use based on the characteristics of each video block. A gating mechanism determines whether to use inter-prediction models, intra-prediction models, or a combination, allowing the system to adapt its complexity based on the specific requirements of each block rather than always using the most complex model.

The system segments the video blocks into different categories based on their characteristics (e.g., temporal coherence vs. spatial coherence) and assigns different prediction models to each segment. This allows efficient combination of inter and intra prediction modes while reducing overall computational complexity by only applying complex predictions where necessary.

Different prediction modes are applied to different blocks based on their local characteristics. The system evaluates each block and applies the most suitable prediction method (inter-prediction, intra-prediction, or neither) locally, optimizing compression efficiency for each region while managing computational resources by avoiding unnecessary complex operations.

Instead of training one comprehensive model to handle all prediction scenarios, the system segments the prediction task into multiple specialized models. Each model is trained on a specific subset of data with particular characteristics, allowing each to achieve high performance on its designated task while the system as a whole manages complexity through selective application.

The system changes the approach by transitioning from a single model with fixed parameters to multiple models with different parameter sets optimized for different conditions. By selecting which model to apply based on block characteristics, the system achieves superior rate-distortion performance without requiring all models to be simultaneously active.