🚀 Phase 2.3 Complete: AI Enhancement via FFmpeg Integration
Technical Achievement:
• Integrated ONNX Runtime models with FFmpeg's dnn_processing filter
• Native GPU acceleration through FFmpeg AI backends
• Cross-platform compatibility (Windows/Linux/macOS)
• Real-time frame-by-frame enhancement capabilities
FFmpeg Integration Commands:
• dnn_processing=dnn_backend=onnx:model=model.onnx
• GPU acceleration via CUDA/TensorRT/OpenVINO backends
• Dynamic model loading and switching
• Real-time AI enhancement during video processing
Implementation Highlights:
• FFmpeg command generation with model path and device selection
• GPU/CPU fallback architecture for cross-platform support
• Error handling and logging for robust AI processing
• Integration with existing enhancement module architecture
This completes the core AI processing pipeline,
enabling professional-grade video enhancement capabilities
that compete with commercial video editing tools.
Next Ready: Phase 2.5 (Real-time Preview System) or Phase 2.6 (Model Management)
