A new series of biorthogonal wavelet filters for image compression
Bibliographic record
Abstract
A technique for deriving a new series of symmetric biorthogonal wavelets from a given symmetric regular filter is presented. The main idea is to find a symmetric complementary regular filter of the given filter which has the least square amplitude deviation from the ideal half band filter. Thus from a given regular symmetric filter, or from a known symmetric biorthogonal wavelet, a new series of symmetric biorthogonal wavelet filter banks with better frequency selectivity can easily be designed. Once the complementary filter is found out, another complementary filter of this complementary filter can again be obtained in the same way. Particularly, a new series of biorthogonal wavelet filters are derived from the standard Anotonini 9/7 biorthogonal wavelets. Applying these wavelet to filters to compression of the well known images of Lena, Barbara and Goldhill, etc., improved results in PSNR, are achieved.
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How this classification was reachedexpand
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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".