Hyperprior Video Compression with Channel Attention and Optical-Flow Coding

The patent replaces manual mechanical design of video coding modules with an automated deep learning-based system. A neural network model is trained to automatically generate optimized coding parameters and structures, substituting the manual design process with an intelligent system that can handle complex optimization tasks and meet demanding application requirements.

The patent employs parameter change principles by training neural networks with varying hyperparameters and architectural configurations to optimize codec performance. The system automatically adjusts multiple parameters including network depth, filter sizes, and loss function weights to achieve optimal performance for high-resolution, high-frame rate, and low-latency applications.

The patent segments the video compression process into multiple stages handled by different neural network components. The encoder network processes input frames through multiple convolutional layers and residual blocks, while the decoder network reconstructs compressed data. This segmentation allows each component to be optimized independently for its specific function, improving overall efficiency while managing computational resources.

The patent applies partial action by using selective attention mechanisms and residual connections that process only the most important features and information at each stage. The network performs partial transformations rather than complete processing, reducing computational burden while maintaining sufficient compression accuracy for high-resolution and low-latency applications.