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:
4
Codebook Size:
256 entries × 16D
Encoder Input:
[None, 1, 320, 1]
Encoder Output:
[None, 1, 20, 16]
Quality Metrics
Time Domain
Metric
Mean
Median
P90
PRD (%)
12.4496
5.4119
25.0669
RMSE
0.0629
0.0476
0.0905
Cosine Similarity
0.9663
0.9986
0.9993
Spectral
Band Total Relative Error (median):
0.0486
Bitrate
Codec CR (uniform):
16.0x
Codec CR (learned prior):
16.30x
Usage
Python (compressionkit runtime)
from compressionkit.runtime import RVQCodec
codec = RVQCodec.from_pretrained("Ambiq/compressionkit-ppg-16x")
# 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.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
@software{compressionkit,
author = {Ambiq AI},
title = {compressionKIT: Signal Compression for Edge AI},
url = {https://github.com/AmbiqAI/compressionkit}
}
Runs of Ambiq compressionkit-ppg-16x on huggingface.co
5
Total runs
0
24-hour runs
-1
3-day runs
-5
7-day runs
-49
30-day runs
More Information About compressionkit-ppg-16x huggingface.co Model
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