Gray-Scale Fingerprint Image Compression Based on the Hybrid-NRCT
Bibliographic record
Abstract
As a directional multiresolution image representation, the contourlet transform can efficiently capture curved and oriented geometrical structures in images. However, the contourlet transform has the drawback of a 4/3 redundancy in its oversampling ratio. Recently, Zhang and Moloney have developed a nonredundant version of the contourlet transform, called the nonredundant contourlet transform (NRCT) and have demonstrated that this 4/3 redundancy can be eliminated by the NRCT. With the advantages of critical sampling and perfect reconstruction, the NRCT is suitable for tracking and efficiently coding oriented structure in images, such as the texture of ridges in fingerprint images. Moreover, as an extension of the wavelet transform, the NRCT is easily compatible with the wavelet transform. A new transform which combines the NRCT with the wavelet transform is called the hybrid-NRCT. This paper proposes a compression scheme for fingerprint images using the hybrid-NRCT, and compares its performance with other transform-based fingerprint image compression schemes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".