video-processor/docs/reference/ROADMAP.md

# Advanced Video Features Roadmap

Building on the existing production-grade 360° video processing and multi-pass encoding foundation.

## 🎯 Phase 1: AI-Powered Video Analysis

### Content Intelligence Engine
**Leverage existing metadata extraction + add ML analysis**

```python
# New: src/video_processor/ai/content_analyzer.py
class VideoContentAnalyzer:
    """AI-powered video content analysis and scene detection."""
    
    async def analyze_content(self, video_path: Path) -> ContentAnalysis:
        """Comprehensive video content analysis."""
        return ContentAnalysis(
            scenes=await self._detect_scenes(video_path),
            objects=await self._detect_objects(video_path),
            faces=await self._detect_faces(video_path),
            text=await self._extract_text(video_path),
            audio_features=await self._analyze_audio(video_path),
            quality_metrics=await self._assess_quality(video_path),
        )
```

**Integration with Existing 360° Pipeline**
- Extend `Video360Detection` with AI confidence scoring
- Smart thumbnail selection based on scene importance
- Automatic 360° viewing angle optimization

### Smart Scene Detection
**Build on existing sprite generation**

```python
# Enhanced: src/video_processor/core/thumbnails.py
class SmartThumbnailGenerator(ThumbnailGenerator):
    """AI-enhanced thumbnail generation with scene detection."""
    
    async def generate_smart_thumbnails(
        self, video_path: Path, scene_analysis: SceneAnalysis
    ) -> list[Path]:
        """Generate thumbnails at optimal scene boundaries."""
        # Use existing thumbnail infrastructure + AI scene detection
        optimal_timestamps = scene_analysis.get_key_moments()
        return await self.generate_thumbnails_at_timestamps(optimal_timestamps)
```

## 🎯 Phase 2: Next-Generation Codecs

### AV1 Support
**Extend existing multi-pass encoding architecture**

```python
# Enhanced: src/video_processor/core/encoders.py  
class VideoEncoder:
    def _encode_av1(self, input_path: Path, output_dir: Path, video_id: str) -> Path:
        """Encode video to AV1 using three-pass encoding."""
        # Leverage existing two-pass infrastructure
        # Add AV1-specific optimizations for 360° content
        quality = self._quality_presets[self.config.quality_preset]
        av1_multiplier = self._get_av1_bitrate_multiplier()
        
        return self._multi_pass_encode(
            codec="libaom-av1",
            passes=3,  # AV1 benefits from three-pass
            quality_preset=quality,
            bitrate_multiplier=av1_multiplier
        )
```

### HDR Support Integration
**Build on existing quality preset system**

```python
# New: src/video_processor/core/hdr_processor.py
class HDRProcessor:
    """HDR video processing with existing quality pipeline."""
    
    def process_hdr_content(
        self, video_path: Path, hdr_metadata: HDRMetadata
    ) -> ProcessedVideo:
        """Process HDR content using existing encoding pipeline."""
        # Extend existing quality presets with HDR parameters
        enhanced_presets = self._enhance_presets_for_hdr(
            self.config.quality_preset, hdr_metadata
        )
        return self._encode_with_hdr(enhanced_presets)
```

## 🎯 Phase 3: Streaming & Real-Time Processing

### Adaptive Streaming
**Leverage existing multi-format output**

```python
# New: src/video_processor/streaming/adaptive.py
class AdaptiveStreamProcessor:
    """Generate adaptive streaming formats from existing encodings."""
    
    async def create_adaptive_stream(
        self, video_path: Path, existing_outputs: list[Path]
    ) -> StreamingPackage:
        """Create HLS/DASH streams from existing MP4/WebM outputs."""
        # Use existing encoded files as base
        # Generate multiple bitrate ladders
        return StreamingPackage(
            hls_playlist=await self._create_hls(existing_outputs),
            dash_manifest=await self._create_dash(existing_outputs),
            thumbnail_track=await self._create_thumbnail_track(),
        )
```

### Live Stream Integration
**Extend existing Procrastinate task system**

```python
# Enhanced: src/video_processor/tasks/streaming_tasks.py
@app.task(queue="streaming")
async def process_live_stream_segment(
    segment_path: Path, stream_config: StreamConfig
) -> SegmentResult:
    """Process live stream segments using existing pipeline."""
    # Leverage existing encoding infrastructure
    # Add real-time optimizations
    processor = VideoProcessor(stream_config.to_processor_config())
    return await processor.process_segment_realtime(segment_path)
```

## 🎯 Phase 4: Advanced 360° Enhancements

### Multi-Modal 360° Processing
**Build on existing sophisticated 360° pipeline**

```python
# Enhanced: src/video_processor/utils/video_360.py
class Advanced360Processor(Video360Utils):
    """Next-generation 360° processing capabilities."""
    
    async def generate_interactive_projections(
        self, video_path: Path, viewing_preferences: ViewingProfile
    ) -> Interactive360Package:
        """Generate multiple projection formats for interactive viewing."""
        # Leverage existing projection math
        # Add interactive navigation data
        return Interactive360Package(
            equirectangular=await self._process_equirectangular(),
            cubemap=await self._generate_cubemap_faces(),
            viewport_optimization=await self._optimize_for_vr_headsets(),
            navigation_mesh=await self._create_navigation_data(),
        )
```

### Spatial Audio Integration
**Extend existing audio processing**

```python
# New: src/video_processor/audio/spatial.py
class SpatialAudioProcessor:
    """360° spatial audio processing."""
    
    async def process_ambisonic_audio(
        self, video_path: Path, audio_format: AmbisonicFormat
    ) -> SpatialAudioResult:
        """Process spatial audio using existing audio pipeline."""
        # Integrate with existing FFmpeg audio processing
        # Add ambisonic encoding support
        return await self._encode_spatial_audio(audio_format)
```

## 🎯 Implementation Strategy

### Phase 1 Priority: AI Content Analysis
**Highest ROI - builds directly on existing infrastructure**

1. **Scene Detection API**: Use OpenCV (already dependency) + ML models
2. **Smart Thumbnail Selection**: Enhance existing thumbnail generation
3. **360° AI Integration**: Extend existing 360° detection with confidence scoring

### Technical Approach
```python
# Integration point with existing system
class EnhancedVideoProcessor(VideoProcessor):
    """AI-enhanced video processor building on existing foundation."""
    
    def __init__(self, config: ProcessorConfig, enable_ai: bool = True):
        super().__init__(config)
        if enable_ai:
            self.content_analyzer = VideoContentAnalyzer()
            self.smart_thumbnail_gen = SmartThumbnailGenerator(config)
        
    async def process_with_ai(self, video_path: Path) -> EnhancedProcessingResult:
        """Enhanced processing with AI analysis."""
        # Use existing processing pipeline
        standard_result = await super().process_video(video_path)
        
        # Add AI enhancements
        if self.content_analyzer:
            ai_analysis = await self.content_analyzer.analyze_content(video_path)
            enhanced_thumbnails = await self.smart_thumbnail_gen.generate_smart_thumbnails(
                video_path, ai_analysis.scenes
            )
            
        return EnhancedProcessingResult(
            standard_output=standard_result,
            ai_analysis=ai_analysis,
            smart_thumbnails=enhanced_thumbnails,
        )
```

### Development Benefits
- **Zero Breaking Changes**: All enhancements extend existing APIs
- **Optional Features**: AI features are opt-in, core pipeline unchanged
- **Dependency Isolation**: New features use same optional dependency pattern
- **Testing Integration**: Leverage existing comprehensive test framework

### Next Steps
1. **Start with Scene Detection**: Implement basic scene boundary detection using OpenCV
2. **Integrate with Existing Thumbnails**: Enhance thumbnail selection with scene analysis  
3. **Add AI Configuration**: Extend ProcessorConfig with AI options
4. **Comprehensive Testing**: Use existing test framework for AI features

This roadmap leverages the excellent existing foundation while adding cutting-edge capabilities that provide significant competitive advantages.