# Customer evidence

Use these recorded scorecards to shortlist codec configurations, then evaluate them on representative signals from your application. Compression ratio (CR) describes payload reduction; it does not describe reconstruction quality by itself.

## What these results cover

These tables are recorded evaluation snapshots, not a live inventory of downloadable models. Sample counts and metric definitions must be read alongside the values. Refreshing the site does not run an evaluation.

- **PPG:** 5-second frames at 64 Hz. Sample counts vary by ratio.
- **ECG:** 2-second frames at 256 Hz.
- **Quality:** this page uses noise-aware scorecards. The model pages also show a separate validation and long-recording evaluation; those values should not be treated as the same run.
- **Package size:** encoder, decoder, and codebook files. These sizes do not measure runtime RAM, firmware size, or inference latency.

## Compression and quality

Lower error is better within the same evaluation. **N** is the reported sample count. A dash means the measurement was not reported, not zero.

### PPG quality

### ECG quality

## Package footprint

The reported total excludes validation samples and documentation. Component sizes are rounded independently, so their displayed sum may differ from the total.

### PPG files

### ECG files

## Detailed signal measurements

These metrics help investigate differences that a single waveform score can miss. Definitions and measurement limitations are in the [validation scorecard](https://ambiqai.github.io/compressionkit/validation-scorecard/).

### PPG detail

### ECG detail

## Choose a candidate

1. Shortlist ratios that meet your payload budget.
2. Inspect the waveform and heart-rate errors together. Check sample counts and noise conditions before comparing rows.
3. Examine the [PPG](https://ambiqai.github.io/compressionkit/models/ppg/) or [ECG](https://ambiqai.github.io/compressionkit/models/ecg/) plots and the detailed [PPG](https://ambiqai.github.io/compressionkit/methods/cr_vs_fidelity_ppg/) or [ECG](https://ambiqai.github.io/compressionkit/methods/cr_vs_fidelity_ecg/) noise buckets.
4. Run the [evaluation notebook](https://ambiqai.github.io/compressionkit/guides/02_evaluate_on_your_data/) on your recordings, then validate a [deploy package](https://ambiqai.github.io/compressionkit/deployment/).

No fixed ratio is recommended for a clinical or product use case by these aggregate measurements alone.