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:
2
Codebook Size:
256 entries × 16D
Encoder Input:
[None, 1, 320, 1]
Encoder Output:
[None, 1, 160, 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 (%)
0.62
Truth PRD at native noise (%)
34.35
Faithful PRD vs input (%)
2.59
PRD degradation slope (PRD%/dB)
4.40
PRD at 0 dB SNR (%)
75.93
PRD at -6 dB SNR (%)
104.76
Pure-noise imprint autocorr
0.2853
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 (%)
2.5884
0.7868
3.3223
RMSE
0.0129
0.0078
0.0251
Cosine Similarity
0.9969
1.0000
1.0000
Spectral
Band Total Relative Error (median):
0.0091
Bitrate
Encoder Precision Parity
Difference from FP32 reconstruction on a disjoint real-data holdout; lower is better.
Encoder
P90 PRD
Worst PRD
Status
int8
3.05%
6.13%
recommended
fp16
0.17%
0.57%
recommended
int16x8
0.66%
1.75%
recommended
Usage
Python (compressionkit runtime)
from compressionkit.runtime import RVQCodec
codec = RVQCodec.from_pretrained("Ambiq/compressionkit-ppg-2x-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-2x-v1.1 on huggingface.co
105
Total runs
29
24-hour runs
36
3-day runs
92
7-day runs
92
30-day runs
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