Generation Super Sampling Using Autoencoders for Fixed Frame Rates

The system performs preliminary actions by pre-rendering multiple real frames in advance and pre-training the autoencoder model beforehand. The autoencoder is trained offline on large datasets of image streams, so during runtime it can rapidly generate synthetic frames without requiring real-time complex processing. This preliminary preparation enables the system to maintain high frame rates with reduced latency during actual operation.

The patent replaces the mechanical rendering system with an intelligent prediction system. Instead of relying solely on the GPU to render every frame mechanically, the autoencoder neural network substitutes this mechanical process by predicting and generating synthetic frames intelligently. The autoencoder learns the temporal patterns and motion dynamics from training data, enabling it to generate realistic intermediate frames without requiring the GPU to perform full rendering operations for each frame, thus reducing processing time while maintaining frame rate.

The system implements feedback mechanisms by comparing the generated synthetic frames with the actual rendered frames and adjusting the generation process accordingly. The autoencoder is trained using feedback from reconstruction errors and motion prediction accuracy, continuously refining its ability to generate visually consistent frames. This feedback loop ensures that synthetic frames blend seamlessly with real frames, reducing visual artifacts like tearing and hitching while maintaining high frame rates.