Looking at the vidkompy codebase, I can identify several performance bottlenecks where Numba could provide significant speedups. Let me analyze the key areas:

## 1. Major Performance Bottlenecks

### 1.1. **DTW Cost Matrix Computation** (Highest Impact)
In `src/vidkompy/core/dtw_aligner.py`, the `_build_dtw_matrix()` method has nested loops that are prime candidates for Numba:

```python
# Current bottleneck in DTWAligner._build_dtw_matrix()
for i in range(1, n_fg + 1):
    j_start = max(1, i - self.window)
    j_end = min(n_bg + 1, i + self.window)
    for j in range(j_start, j_end):
        # Cost computation and DTW recursion
        similarity = compare_func(fg_fp, bg_fp)
        cost = 1.0 - similarity
        dtw[i, j] = cost + min(
            dtw[i - 1, j],      # Skip bg frame
            dtw[i, j - 1],      # Skip fg frame  
            dtw[i - 1, j - 1],  # Match frames
        )
```

### 1.2. **Frame Fingerprint Similarity Comparisons**
The fingerprint comparison operations in `FrameFingerprinter.compare_fingerprints()`:

```python
# Hamming distance calculations for multiple hash types
for name in fp1:
    if name == "histogram":
        score = cv2.compareHist(fp1["histogram"], fp2["histogram"], cv2.HISTCMP_CORREL)
    else:
        distance = cv2.norm(h1, h2, cv2.NORM_HAMMING)
        similarity = 1.0 - (distance / max_bits)
```

### 1.3. **Multi-Resolution Drift Correction**
In `multi_resolution_aligner.py`, the drift correction loops:

```python
# apply_drift_correction() with polynomial fitting
for seg_idx in range(num_segments):
    # Polynomial regression and blending operations
    coeffs = np.polyfit(segment_indices, segment_mapping, self.config.poly_degree)
    poly = np.poly1d(coeffs)
    expected_segment_progression = poly(segment_indices)
```

## 2. Numba Optimization Strategy## Expected Performance Gains

Based on the codebase analysis, here are the areas where Numba could provide the most significant speedups:

### 2.1. High Impact (5-20x speedup):
1. **DTW Cost Matrix Computation** - The nested loops in `_build_dtw_matrix()` are perfect for Numba
2. **Frame fingerprint distance calculations** - Hamming distance computations across many frame pairs
3. **Multi-resolution pyramid processing** - Mathematical operations with array manipulations

### 2.2. Medium Impact (2-5x speedup):
1. **Drift correction algorithms** - Polynomial fitting and blending operations
2. **Border mask creation and application** - Array operations with conditional logic
3. **Interpolation functions** - Mathematical computations for frame mapping

### 2.3. Where Numba Won't Help:
- **OpenCV operations** (already optimized)
- **FFmpeg video I/O** (external library)
- **File system operations**
- **Object-oriented code with complex data structures**

## 3. Implementation Priority

1. **Start with DTW optimization** - Replace `DTWAligner._build_dtw_matrix()` first as it's the biggest bottleneck
2. **Add fingerprint batch processing** - Optimize the similarity comparison loops
3. **Optimize drift correction** - Replace the polynomial fitting loops in multi-resolution aligner
4. **Add mask operation optimizations** - Speed up border mode processing

## 4. Integration Requirements

Add to `pyproject.toml`:
```toml
dependencies = [
    # ... existing dependencies
    "numba>=0.58.0",
]
```

The optimizations would be most effective for the **precise engine** since it does more intensive computations, but the **fast engine** would also benefit from faster DTW and fingerprint operations.

**Note**: The first run will be slower due to JIT compilation, but subsequent runs will be much faster. Consider adding a warm-up phase or pre-compilation step for production use.

Would you like me to show how to integrate any specific optimization into the existing codebase?