---
license: other
license_name: ambiq-model-weights-license
license_link: https://github.com/AmbiqAI/compressionkit/blob/main/LICENSE-MODEL-WEIGHTS.md
library_name: compressionkit
pipeline_tag: other
tags:
  - compressionkit
  - signal-compression
  - ppg
  - rvq
  - tflite
  - edge-ai
---

# compressionkit-ppg-2x

A **PPG** signal compression codec using Residual Vector Quantization (RVQ), optimized for edge and wearable devices.

## Model Details

- **Modality:** PPG
- **Sample Rate:** 64 Hz
- **Compression Ratio:** 2x
- **Quantization:** INT8
- **RVQ Levels:** 4
- **Codebook Size:** 256 entries × 16D
- **Encoder Input:** `[None, 1, 320, 1]`
- **Encoder Output:** `[None, 1, 160, 16]`

## Quality Metrics

### Time Domain

| Metric | Mean | Median | P90 |
|--------|------|--------|-----|
| PRD (%) | 6.1800 | 2.2707 | 10.7564 |
| RMSE | 0.0241 | 0.0199 | 0.0386 |
| Cosine Similarity | 0.9912 | 0.9998 | 0.9999 |

### Spectral

- **Band Total Relative Error (median):** 0.0263

### Bitrate

- **Codec CR (uniform):** 2.0x
- **Codec CR (learned prior):** 3.11x

## Usage

### Python (compressionkit runtime)

```python
from compressionkit.runtime import RVQCodec

codec = RVQCodec.from_pretrained("Ambiq/compressionkit-ppg-2x")

# Encode: float32 signal → RVQ indices
indices = codec.encode(signal)

# Decode: RVQ indices → reconstructed signal
recon = codec.decode(indices)
```

### Local deployment directory

```python
codec = RVQCodec("path/to/deploy/")
```

## Files

| File | Description |
|------|-------------|
| `encoder_int8.tflite` | INT8 quantized encoder (on-device) |
| `encoder.h` | C header for encoder |
| `decoder_float32.tflite` | Float32 decoder (server-side evaluation) |
| `decoder_int8.tflite` | INT8 decoder (optional, on-device) |
| `codebook.npz` | RVQ codebook tables |
| `codebook.h` | C header for codebook |
| `config.json` | Deployment manifest |
| `sample_stimulus.npz` | Synthetic test data |
| `quality_scorecard.json` | Full evaluation metrics |

## Dataset & License

Training data: MESA (NSRR restricted). Sample data uses synthetic physiokit waveforms only — no patient data is redistributed.

Model weights are released under the **Ambiq Model Weights License** — deployment is restricted to Ambiq silicon devices. See `LICENSE-MODEL-WEIGHTS.md` for full terms.

## Citation

```bibtex
@software{compressionkit,
  author = {Ambiq AI},
  title = {compressionKIT: Signal Compression for Edge AI},
  url = {https://github.com/AmbiqAI/compressionkit}
}
```