NOISY SIGNAL COMPRESSION BY WAVELET TRANSFORM WITH OPTIMAL DOWNSAMPLING
Bibliographic record
Abstract
This paper presents a wavelet transform (WT) based on simultaneous de-noising and compression scheme for noisy signal. Due to the downsampling in decomposition process, the orthogonal wavelet transform (OWT) is translation variant, which significantly hinders its performance in coding and denoising. In this paper the wavelet bintree decomposition (WBD), which is equivalent to a translation invariant WT, is first formed and an optimal downsampling route is then traversed among all the routes of the bintree. The WT with the optimal route would most effectively decorrelate and compactly represent the signal. During the process of noisy signal encoding, wavelet thresholding based denoising is performed. Thresholding is similar to the quantization of a zero-zone in lossy encoding procedure. We applied a signal-adaptive threshold to the wavelet coefficients and quantized the coefficients outside the zero-zone. Experiments show that the proposed scheme significantly outperforms the OWT-based method.
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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) | 0.001 | 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".