Neural Network Video Filter Partitioning

The patent replaces traditional mechanical filtering techniques (deblocking filter, sample adaptive offset filter, adaptive loop filter) with a neural network-based filtering system. The neural network model learns optimal filtering operations from training data, substituting hand-crafted filtering algorithms with data-driven intelligent filtering that adapts to different video content characteristics, thereby improving compression efficiency while managing complexity through learned patterns.

The patent changes the parameters and structure of filtering models by introducing neural network architectures with multiple layers, filters, and activation functions. The system dynamically adjusts filtering parameters based on input video characteristics, transforming fixed-parameter traditional filters into adaptive neural network filters that optimize performance for different content types and quality requirements.

The patent segments the video coding process into distinct neural network filtering stages, including deblocking filtering, sample adaptive offset filtering, and adaptive loop filtering, each handled by specialized neural network components. This segmentation allows the system to apply appropriate complexity to each stage independently, optimizing overall performance while managing computational load through distributed processing architecture.

The patent implements partial neural network filtering by selectively applying neural network-based filtering only to specific video regions or blocks that benefit most from it, rather than uniformly applying complex neural networks to all video content. This approach achieves improved compression quality where needed while reducing overall computational complexity for simpler video regions.