<title>Fast embedded image coding technique using wavelet difference reduction</title>
Bibliographic record
Abstract
We propose two modi ed versions of the original wavelet di erence reduction (WDR) algorithm proposed by Tian and Wells. The rst algorithm encodes an image without entropy coding and achieves comparable PSNR performance with the original WDR at low bit rate while exceeding the PSNR performance of original WDR algorithm at medium to high bitrates. The second algorithm achieves slightly higher PSNR performance than that of original WDR with similar computational complexity. The modification to the original WDR is based on the observation that encoding the symbol streams generated in the sorting pass (significance map) and re nement pass (magnitudes) separately can be beneficial. We can either encode the symbol streams under different contexts using entropy coder, or output the raw symbol streams without entropy coding stage. It is shown in the experiments that up to 1/3 of both the encoding time and the decoding time can be saved without sacrificing any PSNR performance when compared to the original WDR.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".