Compression-Aware Video Super-Resolution Neural Network Training

The patent merges the super-resolution neural network with the video encoding module by integrating differentiable units (prediction unit, transform unit, quantization unit, inverse quantization unit, and inverse transform unit) directly into the neural network architecture. This allows the neural network to learn compression-aware representations that inherently account for subsequent encoding losses, eliminating the need for separate compression steps that cause quality degradation.

The patent performs preliminary action by training the neural network to anticipate and compensate for compression losses before the actual video transmission occurs. The loss function incorporates metrics that predict how the generated high-resolution video will be affected by subsequent encoding, allowing the network to pre-adjust its output to minimize final quality loss after compression.

The patent combines multiple functional units (prediction, transform, quantization, inverse quantization, and inverse transform units) into a unified differentiable architecture that can be trained end-to-end. This integration, while increasing initial complexity, enables the system to learn compression-optimized representations that significantly reduce quality loss, ultimately simplifying the overall pipeline by eliminating separate post-processing compression steps.

The patent changes the training parameters by incorporating a composite loss function that includes traditional pixel-wise loss terms along with additional terms that account for compression artifacts and perceptual quality metrics. This parameter change in the loss function guides the neural network to generate videos that are inherently more robust to compression, improving quality without requiring separate compression optimization steps.