Adaptive SPIHT for image coding based on curved wavelet transform
Bibliographic record
Abstract
The curved wavelet transform performs 1-D filtering along curves and exploits orientation features of edges and lines in an image to improve the compactness of the wavelet transform. This paper investigates the issue of efficient data organization and representation of the curved wavelet coefficients. We present an adaptive zero-tree structure that exploits the cross-subband similarity of the curved wavelet transform. The child positions in the adaptive zero-tree structure are not restricted to a square of 2x2 pixels and they vary with the curves along which the WT is performed. Five child patterns have been determined according to different combination of curve orientations. A new image coder, using the curved wavelet transform, is then developed based on this adaptive zero-tree structure and the set partitioning technique. Experimental results using synthetic and natural images show the effectiveness of the proposed adaptive zero-tree structure for encoding of the curved wavelet coefficients. The coding gain of the proposed coder can be as higher as 1.2dB in terms of <i>PSNR</i> compared to the <i>SPIHT</i> coder.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 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".