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:
16x
Quantization:
INT8
RVQ Levels:
2
Codebook Size:
256 entries × 16D
Encoder Input:
[None, 1, 320, 1]
Encoder Output:
[None, 1, 20, 16]
Release Provenance
Release kind: corrected_export_without_retraining.
Export source commit:
7a5443834ec50e41f41d4ff2b1c2f27e73b1acf7
.
Physiological scorecard: historical; not reevaluated for this release.
Packet compatibility: corrected RVQ level counts; bind packets to exact repository and revision.
Pretraining ancestry: not independently audited during export repair.
See
release_provenance.json
for checkpoint hashes, replaced revision, and sample provenance.
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.
Metric
Value
Truth PRD vs clean (%)
5.75
Truth PRD at native noise (%)
34.94
Faithful PRD vs input (%)
7.54
PRD degradation slope (PRD%/dB)
4.38
PRD at 0 dB SNR (%)
76.05
PRD at -6 dB SNR (%)
104.88
Pure-noise imprint autocorr
0.2868
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 (%)
7.5428
5.2305
9.1608
RMSE
0.0553
0.0495
0.0800
Cosine Similarity
0.9893
0.9987
0.9993
Spectral
Band Total Relative Error (median):
0.0259
Bitrate
Encoder Precision Parity
Difference from FP32 reconstruction on a disjoint real-data holdout; lower is better.
Encoder
P90 PRD
Worst PRD
Status
int8
6.70%
12.61%
recommended
fp16
0.00%
4.63%
recommended
int16x8
3.08%
8.09%
recommended
Usage
Python (compressionkit runtime)
from compressionkit.runtime import RVQCodec
codec = RVQCodec.from_pretrained("Ambiq/compressionkit-ppg-16x-v1.1")
# 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
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}
}
Runs of Ambiq compressionkit-ppg-16x-v1.1 on huggingface.co
75
Total runs
1
24-hour runs
18
3-day runs
75
7-day runs
75
30-day runs
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