<title>Minimization of edge effects in images using an extrapolated discrete wavelet transform</title>
Bibliographic record
Abstract
The discrete wavelet transform(DWT) is a tool extensively used in image processing algorithms. It can be used to decorrelate information from the original image, which can thus help in compressing the data for storage, transmission or other post-processing purposes. However, the finite nature of such images gives rise to edge artifacts in the reconstructed data. A commonly used technique to overcome this problem is a symmetric extension of the image, which can preserve zeroth order continuity in the data. This still produces undesirable edge artifacts in derivatives and subsampled versions of the image. In this paper we present an extension to Williams and Amaratunga's work of extrapolating the image data using a polynomial extrapolation technique before performing the forward or inverse DWT for biorthogonal wavelets. Comaparitive results of reconstructed data, with individual subband reconstruction as well as using the embedded zerotree coding (EZC) scheme, are also presented for both the aforementioned techniques.
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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 | 0.000 | 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) | 0.011 | 0.005 |
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".