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:
4
Codebook Size:
256 entries × 16D
Encoder Input:
[None, 1, 320, 1]
Encoder Output:
[None, 1, 160, 16]
Quality Metrics
Time Domain
Metric
Mean
Median
P90
PRD (%)
6.1800
2.2707
10.7564
RMSE
0.0241
0.0199
0.0386
Cosine Similarity
0.9912
0.9998
0.9999
Spectral
Band Total Relative Error (median):
0.0263
Bitrate
Codec CR (uniform):
2.0x
Codec CR (learned prior):
3.11x
Usage
Python (compressionkit runtime)
from compressionkit.runtime import RVQCodec
codec = RVQCodec.from_pretrained("Ambiq/compressionkit-ppg-2x")
# 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-2x on huggingface.co
12
Total runs
0
24-hour runs
-2
3-day runs
-12
7-day runs
-59
30-day runs
More Information About compressionkit-ppg-2x huggingface.co Model
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