Neural Network Video Filtering with Bit-Depth Signaling

The patent replaces traditional mechanical/video coding systems with a neural network-based filter system. The neural network filter processes video data through learned representations, substituting conventional coding mechanisms with AI-driven processing to achieve superior coding efficiency and effectiveness.

The patent introduces bit depth parameters and patch size parameters as controllable variables in the neural network filter processing. By adjusting these parameters (e.g., different bit depths for color components, varying patch sizes), the system optimizes coding performance for different video characteristics and requirements.

The patent implements dynamic parameter adjustment where the neural network filter can adaptively select bit depths and patch sizes based on video content characteristics. This dynamic behavior allows the system to optimize coding efficiency for each specific video segment while managing computational complexity through content-aware adaptation.

The system performs preliminary analysis of video data to determine optimal bit depth and patch size parameters before applying the neural network filter. This preliminary action enables efficient parameter selection that balances coding performance with computational requirements, avoiding unnecessary complexity for simple video content.