Ambiq / compressionkit-ppg-32x-v1.0

compressionkit-ppg-32x

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: 32.0x
• Quantization: INT8
• RVQ Levels: 2
• Codebook Size: 256 entries × 16D
• Encoder Input: [None, 1, 320, 1]
• Encoder Output: [None, 1, 20, 16]

Quality Metrics

Fidelity & Robustness

Both fidelity yardsticks are reported so the codec is judged fairly: faithfulness is PRD vs the recorded (still-noisy) input, while truth fidelity is PRD vs clean ground truth. Lower is better.

Time Domain

PRD here is faithfulness (vs the recorded input); see Fidelity & Robustness above for the clean-truth and noise-regime view.

Spectral

• Band Total Relative Error (median): 0.0627

Bitrate

Usage

Python (compressionkit runtime)

from compressionkit.runtime import RVQCodec

codec = RVQCodec.from_pretrained("Ambiq/compressionkit-ppg-32x")

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

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

Local deployment directory

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

Files

Dataset & License

Training data: BIDMC + BUT PPG + PPG-DaLiA + WESAD (all open, no restricted-access dependency). 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

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