Video Encoding Mode Prediction Using Multi-Size Pixel Blocks

The patent replaces the conventional mechanical traversal approach with a neural network-based prediction system. Instead of systematically iterating through all prediction modes to find the optimal one, a convolutional neural network directly predicts the most likely encoding mode based on image characteristics, substituting computational traversal with neural computation.

The neural network acts as an intermediary between image input and encoding mode selection. It processes image features through multiple layers and outputs predicted encoding modes, serving as a mediator that eliminates the need for direct traversal of all possible prediction modes while maintaining selection accuracy.

The neural network is pre-trained on large datasets of images and their corresponding optimal encoding modes. This preliminary training allows the system to make accurate predictions about encoding modes for new images without needing to traverse all possible modes during actual processing, significantly reducing real-time computation time.

The patent substitutes the time-consuming mechanical traversal process with neural network inference. The neural network's trained weights and architecture enable it to directly compute encoding mode predictions in a single forward pass, eliminating the iterative traversal process that would otherwise be required.

The neural network architecture is designed to be universally applicable to various image types and encoding scenarios. By training on diverse datasets and learning general features, the model can handle multiple encoding tasks with a single unified architecture, reducing the need for separate training models for different applications.

The patent employs techniques such as data augmentation, normalization, and adaptive learning rates to manage training complexity. These parameter changes and adjustments allow the model to learn effectively from large datasets without requiring proportional increases in computational resources during training, balancing productivity gains with training feasibility.