Rate distortion optimized curve determination for curved wavelet image coding
Bibliographic record
Abstract
The curved wavelet transform (CWT) was developed to enhance compactness of the wavelet transform (WT) representation. Curve determination is critical for the CWT because a well-defined curve set can increase the performance gain in terms of the rate-distortion (R-D). Conventionally, the image to be encoded is divided into blocks and the curve orientation in each block is independently determined through the minimization of its high-pass CWT energy. In this paper, we propose an R-D optimization algorithm for the curve determination, in which variable block size and the impact of neighboring blocks are taken into account. To reduce the computational cost, an alternative sampling strategy is exploited. Experiment results with natural images show that the proposed algorithm can provide better image quality, measured objectively or subjectively, compared to the conventional CWT coding algorithm. Importantly, the proposed approach overcomes the hurdles of computational cost and optimization at the global level opens the door for further performance enhancements of applications with the CWT.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".