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.

Metric	Value
Truth PRD vs clean (%)	2.66
Truth PRD at native noise (%)	34.43
Faithful PRD vs input (%)	5.10
PRD degradation slope (PRD%/dB)	4.39
PRD at 0 dB SNR (%)	75.99
PRD at -6 dB SNR (%)	104.80
Pure-noise imprint autocorr	0.2859

Time Domain

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

Metric	Mean	Median	P90
PRD vs input — faithfulness (%)	5.0979	2.7274	5.6243
RMSE	0.0306	0.0265	0.0439
Cosine Similarity	0.9917	0.9996	0.9998

Spectral

Band Total Relative Error (median): 0.0153

Bitrate

Encoder Precision Parity

Difference from FP32 reconstruction on a disjoint real-data holdout; lower is better.

Encoder	P90 PRD	Worst PRD	Status
FP16	0.41%	1.82%	recommended
INT16X8	1.38%	3.33%	recommended
INT8	3.72%	7.03%	recommended

Usage

Python (compressionkit runtime)

python

from compressionkit.runtime import RVQCodec

codec = RVQCodec.from_pretrained("Ambiq/compressionkit-ppg-8x-v1.0")

# 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

File	Description
`encoder_int8.tflite`	INT8 quantized encoder (on-device)
`encoder_float32.tflite`	Float32 encoder for browser/server runtimes
`encoder_fp16.tflite`	FP16 encoder variant for supported edge runtimes
`encoder_int16x8.tflite`	INT16x8 encoder variant for supported edge runtimes
`encoder.h`	C header for encoder
`encoder.keras`	Float32 Python reference encoder (training/inspection use)
`decoder_float32.tflite`	Float32 decoder (server-side evaluation)
`decoder_int8.tflite`	INT8 decoder (optional, on-device)
`decoder.keras`	Float32 Python reference decoder (training/inspection use)
`codebook.npz`	RVQ codebook tables
`codebook.h`	C header for codebook
`config.json`	Deployment manifest
`sample_stimulus.npz`	Synthetic test data
`demo_recordings.npz`	10 real, quality-gated browser-demo recordings
`demo_recordings_manifest.json`	Recording provenance, license, and quality metadata
`quality_scorecard.json`	Full evaluation metrics

Dataset & License

Training data provenance is not recorded in this package; sample data uses synthetic physiokit waveforms only — no patient data is redistributed. Demo recordings: BIDMC PPG and Respiration Dataset v1.0.0 (source); real, quality-gated excerpts are released under ODC-By-1.0. https://opendatacommons.org/licenses/by/1-0/

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}
}