An efficient fingerprint image compression technique based on wave atoms decomposition and multistage vector quantization
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Modern fingerprint image compression and reconstruction standards used by the US Federal Bureau of Investigation (FBI) are based upon the popular biorthogonal 9-7 discrete wavelet transform. Multiresolution analysis tools have been successfully applied to fingerprint image compression for more than a decade; we propose a novel fingerprint image compression technique based on wave atoms decomposition and multistage vector quantization. Wave atoms decomposition has been specifically designed for enhanced representation of oscillatory patterns and to convey precise temporal and spatial information. Our proposed compression scheme is based upon multistage vector quantization of processed wave atoms representation of fingerprint images. Wave atoms expansion is processed using mathematical morphological operators to emphasize and retain significant coefficients for transmission. Quantized information is encoded using arithmetic entropy scheme. The proposed image compression standard outperforms other well established methods and achieves PSNR gain up to 8.07 dB in comparison to FBI's wavelet scalar quantization. Data mining, law enforcement, border security, and forensic applications can potentially benefit from our compression scheme.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it